Program Version:EnergyPlus, Version 9.1.0-08d2e308bb, YMD=2021.07.16 12:27
Tabular Output Report in Format: HTML
Building: Ref Bldg Medium Office New2004_v1.3_5.0
Environment: PHILADELPHIA 2014 ** PHILADELPHIA - USA Custom-720304 WMO#=720304
Simulation Timestamp: 2021-07-16 12:27:08
Report: Annual Building Utility Performance Summary
For: Entire Facility
Timestamp: 2021-07-16 12:27:08
Values gathered over 8760.00 hours| Total Energy [GJ] | Energy Per Total Building Area [MJ/m2] | Energy Per Conditioned Building Area [MJ/m2] | |
| Total Site Energy | 1771.25 | 355.52 | 355.52 |
| Net Site Energy | 1771.25 | 355.52 | 355.52 |
| Total Source Energy | 5954.21 | 1195.10 | 1195.10 |
| Net Source Energy | 5954.21 | 1195.10 | 1195.10 |
| Site=>Source Conversion Factor | |
| Electricity | 3.546 |
| Natural Gas | 1.092 |
| District Cooling | 0.848 |
| District Heating | 1.647 |
| Steam | 0.585 |
| Gasoline | 1.050 |
| Diesel | 1.050 |
| Coal | 1.050 |
| Fuel Oil #1 | 1.050 |
| Fuel Oil #2 | 1.050 |
| Propane | 1.050 |
| Other Fuel 1 | 1.000 |
| Other Fuel 2 | 1.000 |
| Area [m2] | |
| Total Building Area | 4982.19 |
| Net Conditioned Building Area | 4982.19 |
| Unconditioned Building Area | 0.00 |
| Electricity [GJ] | Natural Gas [GJ] | Additional Fuel [GJ] | District Cooling [GJ] | District Heating [GJ] | Water [m3] | |
| Heating | 274.48 | 96.98 | 0.00 | 0.00 | 0.00 | 0.00 |
| Cooling | 260.82 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Interior Lighting | 210.48 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Exterior Lighting | 232.69 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Interior Equipment | 594.08 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Exterior Equipment | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Fans | 65.33 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Pumps | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Heat Rejection | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Humidification | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Heat Recovery | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Water Systems | 0.00 | 36.13 | 0.00 | 0.00 | 0.00 | 174.75 |
| Refrigeration | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Generators | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Total End Uses | 1638.14 | 133.11 | 0.00 | 0.00 | 0.00 | 174.75 |
| Subcategory | Electricity [GJ] | Natural Gas [GJ] | Additional Fuel [GJ] | District Cooling [GJ] | District Heating [GJ] | Water [m3] | |
| Heating | General | 274.48 | 96.98 | 0.00 | 0.00 | 0.00 | 0.00 |
| Cooling | General | 260.82 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Interior Lighting | General | 210.48 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Exterior Lighting | Exterior Facade Lighting | 232.69 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Interior Equipment | General | 429.16 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Elevators | 164.92 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
| Exterior Equipment | General | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Fans | Fan Energy | 65.33 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Pumps | General | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Heat Rejection | General | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Humidification | General | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Heat Recovery | General | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Water Systems | Water Heater | 0.00 | 36.13 | 0.00 | 0.00 | 0.00 | 0.00 |
| General | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 174.75 | |
| Refrigeration | General | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Generators | General | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Electricity Intensity [MJ/m2] | Natural Gas Intensity [MJ/m2] | Additional Fuel Intensity [MJ/m2] | District Cooling Intensity [MJ/m2] | District Heating Intensity [MJ/m2] | Water Intensity [m3/m2] | |
| Lighting | 88.95 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| HVAC | 120.61 | 26.72 | 0.00 | 0.00 | 0.00 | 0.04 |
| Other | 119.24 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Total | 328.80 | 26.72 | 0.00 | 0.00 | 0.00 | 0.04 |
| Electricity Intensity [MJ/m2] | Natural Gas Intensity [MJ/m2] | Additional Fuel Intensity [MJ/m2] | District Cooling Intensity [MJ/m2] | District Heating Intensity [MJ/m2] | Water Intensity [m3/m2] | |
| Lighting | 88.95 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| HVAC | 120.61 | 26.72 | 0.00 | 0.00 | 0.00 | 0.04 |
| Other | 119.24 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Total | 328.80 | 26.72 | 0.00 | 0.00 | 0.00 | 0.04 |
| Electricity [GJ] | Percent Electricity [%] | |
| Fuel-Fired Power Generation | 0.000 | 0.00 |
| High Temperature Geothermal* | 0.000 | 0.00 |
| Photovoltaic Power | 0.000 | 0.00 |
| Wind Power | 0.000 | 0.00 |
| Power Conversion | 0.000 | 0.00 |
| Net Decrease in On-Site Storage | 0.000 | 0.00 |
| Total On-Site Electric Sources | 0.000 | 0.00 |
| Electricity Coming From Utility | 1638.142 | 100.00 |
| Surplus Electricity Going To Utility | 0.000 | 0.00 |
| Net Electricity From Utility | 1638.142 | 100.00 |
| Total On-Site and Utility Electric Sources | 1638.142 | 100.00 |
| Total Electricity End Uses | 1638.142 | 100.00 |
| Heat [GJ] | Percent Heat [%] | |
| Water-Side Heat Recovery | 0.00 | |
| Air to Air Heat Recovery for Cooling | 0.00 | |
| Air to Air Heat Recovery for Heating | 0.00 | |
| High-Temperature Geothermal* | 0.00 | |
| Solar Water Thermal | 0.00 | |
| Solar Air Thermal | 0.00 | |
| Total On-Site Thermal Sources | 0.00 |
| Water [m3] | Percent Water [%] | |
| Rainwater Collection | 0.00 | 0.00 |
| Condensate Collection | 0.00 | 0.00 |
| Groundwater Well | 0.00 | 0.00 |
| Total On Site Water Sources | 0.00 | 0.00 |
| - | - | - |
| Initial Storage | 0.00 | 0.00 |
| Final Storage | 0.00 | 0.00 |
| Change in Storage | 0.00 | 0.00 |
| - | - | - |
| Water Supplied by Utility | 174.75 | 100.00 |
| - | - | - |
| Total On Site, Change in Storage, and Utility Water Sources | 174.75 | 100.00 |
| Total Water End Uses | 174.75 | 100.00 |
| Degrees [deltaC] | |
| Tolerance for Zone Heating Setpoint Not Met Time | 0.56 |
| Tolerance for Zone Cooling Setpoint Not Met Time | 0.56 |
| Facility [Hours] | |
| Time Setpoint Not Met During Occupied Heating | 13.33 |
| Time Setpoint Not Met During Occupied Cooling | 5.50 |
| Time Not Comfortable Based on Simple ASHRAE 55-2004 | 3050.17 |
Table of Contents
TopEMISSIONS DATA SUMMARY
| Meter |COMPONENTS OF PEAK ELECTRICAL DEMAND
| Meter |HEATING PART LOAD PERFORMANCE
| VAV_1_HEATC | VAV_2_HEATC | VAV_3_HEATC |COOLING PART LOAD PERFORMANCE
| VAV_1_COOLC DXCOIL | VAV_2_COOLC DXCOIL | VAV_3_COOLC DXCOIL |FAN PART LOAD PERFORMANCE
| VAV_1_FAN | VAV_2_FAN | VAV_3_FAN |Report: Input Verification and Results Summary
For: Entire Facility
Timestamp: 2021-07-16 12:27:08
General| Value | |
| Program Version and Build | EnergyPlus, Version 9.1.0-08d2e308bb, YMD=2021.07.16 12:27 |
| RunPeriod | PHILADELPHIA 2014 |
| Weather File | PHILADELPHIA - USA Custom-720304 WMO#=720304 |
| Latitude [deg] | 40.14 |
| Longitude [deg] | -75.3 |
| Elevation [m] | 92.00 |
| Time Zone | -5.0 |
| North Axis Angle [deg] | 0.00 |
| Rotation for Appendix G [deg] | 0.00 |
| Hours Simulated [hrs] | 8760.00 |
| Total | North (315 to 45 deg) | East (45 to 135 deg) | South (135 to 225 deg) | West (225 to 315 deg) | |
| Gross Wall Area [m2] | 1977.67 | 593.30 | 395.53 | 593.30 | 395.53 |
| Above Ground Wall Area [m2] | 1977.67 | 593.30 | 395.53 | 593.30 | 395.53 |
| Window Opening Area [m2] | 652.83 | 195.85 | 130.57 | 195.85 | 130.57 |
| Gross Window-Wall Ratio [%] | 33.01 | 33.01 | 33.01 | 33.01 | 33.01 |
| Above Ground Window-Wall Ratio [%] | 33.01 | 33.01 | 33.01 | 33.01 | 33.01 |
| Total | North (315 to 45 deg) | East (45 to 135 deg) | South (135 to 225 deg) | West (225 to 315 deg) | |
| Gross Wall Area [m2] | 1977.67 | 593.30 | 395.53 | 593.30 | 395.53 |
| Above Ground Wall Area [m2] | 1977.67 | 593.30 | 395.53 | 593.30 | 395.53 |
| Window Opening Area [m2] | 652.83 | 195.85 | 130.57 | 195.85 | 130.57 |
| Gross Window-Wall Ratio [%] | 33.01 | 33.01 | 33.01 | 33.01 | 33.01 |
| Above Ground Window-Wall Ratio [%] | 33.01 | 33.01 | 33.01 | 33.01 | 33.01 |
| Total | |
| Gross Roof Area [m2] | 1660.73 |
| Skylight Area [m2] | 0.00 |
| Skylight-Roof Ratio [%] | 0.00 |
| Area [m2] | Conditioned (Y/N) | Part of Total Floor Area (Y/N) | Volume [m3] | Multipliers | Above Ground Gross Wall Area [m2] | Underground Gross Wall Area [m2] | Window Glass Area [m2] | Opening Area [m2] | Lighting [W/m2] | People [m2 per person] | Plug and Process [W/m2] | |
| CORE_BOTTOM | 983.54 | Yes | Yes | 2698.04 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.7320 | 22.58 | 37.7664 |
| CORE_MID | 983.54 | Yes | Yes | 2698.04 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.7320 | 22.58 | 5.1190 |
| CORE_TOP | 983.54 | Yes | Yes | 2698.04 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.7320 | 22.58 | 5.1190 |
| FIRSTFLOOR_PLENUM | 1660.73 | Yes | No | 2024.76 | 1.00 | 202.84 | 0.00 | 0.00 | 0.00 | 0.0000 | 0.0000 | |
| MIDFLOOR_PLENUM | 1660.73 | Yes | No | 2024.76 | 1.00 | 202.84 | 0.00 | 0.00 | 0.00 | 0.0000 | 0.0000 | |
| PERIMETER_BOT_ZN_1 | 207.34 | Yes | Yes | 568.77 | 1.00 | 136.92 | 0.00 | 65.28 | 65.28 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_BOT_ZN_2 | 131.26 | Yes | Yes | 360.08 | 1.00 | 91.28 | 0.00 | 43.52 | 43.52 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_BOT_ZN_3 | 207.34 | Yes | Yes | 568.77 | 1.00 | 136.92 | 0.00 | 65.28 | 65.28 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_BOT_ZN_4 | 131.25 | Yes | Yes | 360.05 | 1.00 | 91.28 | 0.00 | 43.52 | 43.52 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_MID_ZN_1 | 207.34 | Yes | Yes | 568.77 | 1.00 | 136.92 | 0.00 | 65.28 | 65.28 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_MID_ZN_2 | 131.26 | Yes | Yes | 360.08 | 1.00 | 91.28 | 0.00 | 43.52 | 43.52 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_MID_ZN_3 | 207.34 | Yes | Yes | 568.77 | 1.00 | 136.92 | 0.00 | 65.28 | 65.28 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_MID_ZN_4 | 131.25 | Yes | Yes | 360.05 | 1.00 | 91.28 | 0.00 | 43.52 | 43.52 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_TOP_ZN_1 | 207.34 | Yes | Yes | 568.77 | 1.00 | 136.92 | 0.00 | 65.28 | 65.28 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_TOP_ZN_2 | 131.26 | Yes | Yes | 360.08 | 1.00 | 91.28 | 0.00 | 43.52 | 43.52 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_TOP_ZN_3 | 207.34 | Yes | Yes | 568.77 | 1.00 | 136.92 | 0.00 | 65.28 | 65.28 | 3.7320 | 22.58 | 5.1190 |
| PERIMETER_TOP_ZN_4 | 131.25 | Yes | Yes | 360.05 | 1.00 | 91.28 | 0.00 | 43.52 | 43.52 | 3.7320 | 22.58 | 5.1190 |
| TOPFLOOR_PLENUM | 1660.73 | Yes | No | 2024.76 | 1.00 | 202.84 | 0.00 | 0.00 | 0.00 | 0.0000 | 0.0000 | |
| Total | 4982.19 | 13667.13 | 1369.16 | 0.00 | 652.83 | 652.83 | 3.7320 | 22.58 | 11.5639 | |||
| Conditioned Total | 4982.19 | 13667.13 | 1369.16 | 0.00 | 652.83 | 652.83 | 3.7320 | 22.58 | 11.5639 | |||
| Unconditioned Total | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ||||||
| Not Part of Total | 4982.19 | 6074.28 | 608.51 | 0.00 | 0.00 | 0.00 | 0.0000 | 0.0000 |
Report: Component Sizing Summary
For: Entire Facility
Timestamp: 2021-07-16 12:27:08
AirTerminal:SingleDuct:VAV:Reheat| Design Size Maximum Air Flow Rate [m3/s] | Design Size Constant Minimum Air Flow Fraction | User-Specified Constant Minimum Air Flow Fraction | Design Size Minimum Air Flow Rate [m3/s] | Design Size Maximum Flow per Zone Floor Area during Reheat [m3/s-m2] | Design Size Maximum Flow Fraction during Reheat [] | |
| CORE_BOTTOM VAV BOX COMPONENT | 2.55 | 0.294479 | 0.300000 | 0.763505 | 0.000776 | 0.300000 |
| CORE_MID VAV BOX COMPONENT | 2.22 | 0.337482 | 0.300000 | 0.666217 | 0.000677 | 0.300000 |
| CORE_TOP VAV BOX COMPONENT | 2.28 | 0.329103 | 0.300000 | 0.683180 | 0.000695 | 0.300000 |
| PERIMETER_BOT_ZN_1 VAV BOX COMPONENT | 0.852165 | 0.200000 | 0.300000 | 0.255650 | 0.001233 | 0.300000 |
| PERIMETER_BOT_ZN_2 VAV BOX COMPONENT | 1.13 | 0.200000 | 0.300000 | 0.339314 | 0.002585 | 0.300000 |
| PERIMETER_BOT_ZN_3 VAV BOX COMPONENT | 0.521918 | 0.302713 | 0.300000 | 0.156575 | 0.000755 | 0.300000 |
| PERIMETER_BOT_ZN_4 VAV BOX COMPONENT | 1.26 | 0.200000 | 0.300000 | 0.377753 | 0.002878 | 0.300000 |
| PERIMETER_MID_ZN_1 VAV BOX COMPONENT | 1.01 | 0.200000 | 0.300000 | 0.303974 | 0.001466 | 0.300000 |
| PERIMETER_MID_ZN_2 VAV BOX COMPONENT | 1.25 | 0.200000 | 0.300000 | 0.376144 | 0.002866 | 0.300000 |
| PERIMETER_MID_ZN_3 VAV BOX COMPONENT | 0.676210 | 0.233643 | 0.300000 | 0.202863 | 0.000978 | 0.300000 |
| PERIMETER_MID_ZN_4 VAV BOX COMPONENT | 1.36 | 0.200000 | 0.300000 | 0.407446 | 0.003104 | 0.300000 |
| PERIMETER_TOP_ZN_1 VAV BOX COMPONENT | 1.06 | 0.200000 | 0.300000 | 0.318831 | 0.001538 | 0.300000 |
| PERIMETER_TOP_ZN_2 VAV BOX COMPONENT | 1.25 | 0.200000 | 0.300000 | 0.375819 | 0.002863 | 0.300000 |
| PERIMETER_TOP_ZN_3 VAV BOX COMPONENT | 0.768052 | 0.205704 | 0.300000 | 0.230416 | 0.001111 | 0.300000 |
| PERIMETER_TOP_ZN_4 VAV BOX COMPONENT | 1.43 | 0.200000 | 0.300000 | 0.429195 | 0.003270 | 0.300000 |
| Design Size Nominal Capacity [W] | |
| CORE_BOTTOM VAV BOX REHEAT COIL | 30883.71 |
| CORE_MID VAV BOX REHEAT COIL | 26948.43 |
| CORE_TOP VAV BOX REHEAT COIL | 27634.54 |
| PERIMETER_BOT_ZN_1 VAV BOX REHEAT COIL | 10861.75 |
| PERIMETER_BOT_ZN_2 VAV BOX REHEAT COIL | 13725.23 |
| PERIMETER_BOT_ZN_3 VAV BOX REHEAT COIL | 10613.78 |
| PERIMETER_BOT_ZN_4 VAV BOX REHEAT COIL | 15280.08 |
| PERIMETER_MID_ZN_1 VAV BOX REHEAT COIL | 12962.44 |
| PERIMETER_MID_ZN_2 VAV BOX REHEAT COIL | 15214.99 |
| PERIMETER_MID_ZN_3 VAV BOX REHEAT COIL | 12758.89 |
| PERIMETER_MID_ZN_4 VAV BOX REHEAT COIL | 16481.15 |
| PERIMETER_TOP_ZN_1 VAV BOX REHEAT COIL | 16732.78 |
| PERIMETER_TOP_ZN_2 VAV BOX REHEAT COIL | 15201.84 |
| PERIMETER_TOP_ZN_3 VAV BOX REHEAT COIL | 16557.24 |
| PERIMETER_TOP_ZN_4 VAV BOX REHEAT COIL | 17360.91 |
| Sum of Air Terminal Maximum Heating Flow Rates [m3/s] | Sum of Air Terminal Minimum Heating Flow Rates [m3/s] | Sum of Air Terminal Maximum Flow Rates [m3/s] | Adjusted Heating Design Air Flow Rate [m3/s] | Adjusted Cooling Design Air Flow Rate [m3/s] | Adjusted Main Design Air Flow Rate [m3/s] | User Heating Air Flow Ratio [] | Calculated Heating Air Flow Ratio [] | Design Supply Air Flow Rate [m3/s] | |
| VAV_1 | 1.89 | 1.89 | 6.31 | 1.89 | 6.31 | 6.31 | 0.300000 | 0.300000 | 6.31 |
| VAV_2 | 1.96 | 1.96 | 6.52 | 1.96 | 6.52 | 6.52 | 0.300000 | 0.300000 | 6.52 |
| VAV_3 | 2.04 | 2.04 | 6.79 | 2.04 | 6.79 | 6.79 | 0.300000 | 0.300000 | 6.79 |
| Maximum Outdoor Air Flow Rate [m3/s] | Minimum Outdoor Air Flow Rate [m3/s] | |
| VAV_1_OA_CONTROLLER | 6.31 | 0.919521 |
| VAV_2_OA_CONTROLLER | 6.52 | 0.919521 |
| VAV_3_OA_CONTROLLER | 6.79 | 0.919521 |
| Design Size High Speed Rated Air Flow Rate [m3/s] | Design Size High Speed Gross Rated Total Cooling Capacity [W] | Design Size High Speed Rated Sensible Heat Ratio | Design Size Low Speed Rated Air Flow Rate [m3/s] | Design Size Low Speed Gross Rated Total Cooling Capacity [W] | Design Size Low Speed Gross Rated Sensible Heat Ratio | |
| VAV_1_COOLC DXCOIL | 6.31 | 128747.62 | 0.764247 | 2.10 | 42911.58 | 0.764247 |
| VAV_2_COOLC DXCOIL | 6.52 | 128751.75 | 0.777297 | 2.17 | 42912.96 | 0.777297 |
| VAV_3_COOLC DXCOIL | 6.79 | 132932.33 | 0.780932 | 2.26 | 44306.35 | 0.780932 |
| Design Size Design Maximum Air Flow Rate [m3/s] | Design Electric Power Consumption [W] | |
| VAV_1_FAN | 6.31 | 11836.23 |
| Design Size Nominal Capacity [W] | |
| VAV_1_HEATC | 38216.32 |
| VAV_2_HEATC | 38030.07 |
| VAV_3_HEATC | 37794.37 |
| Design Size Maximum Flow Rate [m3/s] | |
| VAV_2_FAN | 6.52 |
| VAV_3_FAN | 6.79 |
| Maximum Loop Flow Rate [m3/s] | Plant Loop Volume [m3] | |
| SWHSYS1 | 0.000031 | 0.003744 |
| Design Flow Rate [m3/s] | Design Power Consumption [W] | |
| SWHSYS1 PUMP | 0.000031 | 8.44 |
| Use Side Design Flow Rate [m3/s] | |
| SWHSYS1 WATER HEATER | 0.000031 |
Report: Climatic Data Summary
For: Entire Facility
Timestamp: 2021-07-16 12:27:08
SizingPeriod:DesignDay| Maximum Dry Bulb [C] | Daily Temperature Range [deltaC] | Humidity Value | Humidity Type | Wind Speed [m/s] | Wind Direction | |
| CHICAGO ANN HTG 99.6% CONDNS DB | -20.60 | 0.00 | -20.60 | Wetbulb [C] | 4.90 | 270.00 |
| CHICAGO ANN CLG .4% CONDNS WB=>MDB | 31.20 | 10.70 | 25.50 | Wetbulb [C] | 5.30 | 230.00 |
| Value | |
| None |
Report: Envelope Summary
For: Entire Facility
Timestamp: 2021-07-16 12:27:08
Opaque Exterior| Construction | Reflectance | U-Factor with Film [W/m2-K] | U-Factor no Film [W/m2-K] | Gross Area [m2] | Net Area [m2] | Azimuth [deg] | Tilt [deg] | Cardinal Direction | |
| CORE_BOT_ZN_5_FLOOR | EXT-SLAB | 0.30 | 2.193 | 3.401 | 983.54 | 983.54 | 0.00 | 180.00 | |
| PERIMETER_BOT_PLENUM_WALL_EAST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 40.57 | 40.57 | 90.00 | 90.00 | E |
| PERIMETER_BOT_PLENUM_WALL_NORTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 60.85 | 60.85 | 0.00 | 90.00 | N |
| PERIMETER_BOT_PLENUM_WALL_SOUTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 60.85 | 60.85 | 180.00 | 90.00 | S |
| PERIMETER_BOT_PLENUM_WALL_WEST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 40.57 | 40.57 | 270.00 | 90.00 | W |
| PERIMETER_MID_PLENUM_WALL_EAST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 40.57 | 40.57 | 90.00 | 90.00 | E |
| PERIMETER_MID_PLENUM_WALL_NORTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 60.85 | 60.85 | 0.00 | 90.00 | N |
| PERIMETER_MID_PLENUM_WALL_SOUTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 60.85 | 60.85 | 180.00 | 90.00 | S |
| PERIMETER_MID_PLENUM_WALL_WEST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 40.57 | 40.57 | 270.00 | 90.00 | W |
| PERIMETER_BOT_ZN_1_WALL_SOUTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 136.92 | 71.63 | 180.00 | 90.00 | S |
| PERIMETER_BOT_ZN_1_FLOOR | EXT-SLAB | 0.30 | 2.193 | 3.401 | 207.34 | 207.34 | 180.00 | 180.00 | |
| PERIMETER_BOT_ZN_2_WALL_EAST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 91.28 | 47.75 | 90.00 | 90.00 | E |
| PERIMETER_BOT_ZN_2_FLOOR | EXT-SLAB | 0.30 | 2.193 | 3.401 | 131.26 | 131.26 | 90.00 | 180.00 | |
| PERIMETER_BOT_ZN_3_WALL_NORTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 136.92 | 71.63 | 0.00 | 90.00 | N |
| PERIMETER_BOT_ZN_3_FLOOR | EXT-SLAB | 0.30 | 2.193 | 3.401 | 207.34 | 207.34 | 0.00 | 180.00 | |
| PERIMETER_BOT_ZN_4_WALL_WEST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 91.28 | 47.75 | 270.00 | 90.00 | W |
| PERIMETER_BOT_ZN_4_FLOOR | EXT-SLAB | 0.30 | 2.193 | 3.401 | 131.25 | 131.25 | 270.00 | 180.00 | |
| PERIMETER_MID_ZN_1_WALL_SOUTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 136.92 | 71.63 | 180.00 | 90.00 | S |
| PERIMETER_MID_ZN_2_WALL_EAST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 91.28 | 47.75 | 90.00 | 90.00 | E |
| PERIMETER_MID_ZN_3_WALL_NORTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 136.92 | 71.63 | 0.00 | 90.00 | N |
| PERIMETER_MID_ZN_4_WALL_WEST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 91.28 | 47.75 | 270.00 | 90.00 | W |
| PERIMETER_TOP_ZN_1_WALL_SOUTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 136.92 | 71.63 | 180.00 | 90.00 | S |
| PERIMETER_TOP_ZN_2_WALL_EAST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 91.28 | 47.75 | 90.00 | 90.00 | E |
| PERIMETER_TOP_ZN_3_WALL_NORTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 136.92 | 71.63 | 0.00 | 90.00 | N |
| PERIMETER_TOP_ZN_4_WALL_WEST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 91.28 | 47.75 | 270.00 | 90.00 | W |
| PERIMETER_TOP_PLENUM_WALL_EAST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 40.57 | 40.57 | 90.00 | 90.00 | E |
| PERIMETER_TOP_PLENUM_WALL_NORTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 60.85 | 60.85 | 0.00 | 90.00 | N |
| PERIMETER_TOP_PLENUM_WALL_SOUTH | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 60.85 | 60.85 | 180.00 | 90.00 | S |
| PERIMETER_TOP_PLENUM_WALL_WEST | STEEL FRAME NON-RES EXT WALL | 0.22 | 0.654 | 0.725 | 40.57 | 40.57 | 270.00 | 90.00 | W |
| BUILDING_ROOF | IEAD NON-RES ROOF | 0.30 | 0.358 | 0.376 | 1660.73 | 1660.73 | 0.00 | 0.00 |
| Construction | Glass Area [m2] | Frame Area [m2] | Divider Area [m2] | Area of One Opening [m2] | Area of Multiplied Openings [m2] | Glass U-Factor [W/m2-K] | Glass SHGC | Glass Visible Transmittance | Frame Conductance [W/m2-K] | Divider Conductance [W/m2-K] | Shade Control | Parent Surface | Azimuth [deg] | Tilt [deg] | Cardinal Direction | |
| PERIMETER_BOT_ZN_1_WALL_SOUTH_WINDOW | WINDOW NON-RES FIXED | 65.28 | 0.00 | 0.00 | 65.28 | 65.28 | 3.940 | 0.519 | 0.398 | No | PERIMETER_BOT_ZN_1_WALL_SOUTH | 180.00 | 90.00 | S | ||
| PERIMETER_BOT_ZN_2_WALL_EAST_WINDOW | WINDOW NON-RES FIXED | 43.52 | 0.00 | 0.00 | 43.52 | 43.52 | 3.940 | 0.519 | 0.398 | No | PERIMETER_BOT_ZN_2_WALL_EAST | 90.00 | 90.00 | E | ||
| PERIMETER_BOT_ZN_3_WALL_NORTH_WINDOW | WINDOW NON-RES FIXED | 65.28 | 0.00 | 0.00 | 65.28 | 65.28 | 3.940 | 0.519 | 0.398 | No | PERIMETER_BOT_ZN_3_WALL_NORTH | 0.00 | 90.00 | N | ||
| PERIMETER_BOT_ZN_4_WALL_WEST_WINDOW | WINDOW NON-RES FIXED | 43.52 | 0.00 | 0.00 | 43.52 | 43.52 | 3.940 | 0.519 | 0.398 | No | PERIMETER_BOT_ZN_4_WALL_WEST | 270.00 | 90.00 | W | ||
| PERIMETER_MID_ZN_1_WALL_SOUTH_WINDOW | WINDOW NON-RES FIXED | 65.28 | 0.00 | 0.00 | 65.28 | 65.28 | 3.940 | 0.519 | 0.398 | No | PERIMETER_MID_ZN_1_WALL_SOUTH | 180.00 | 90.00 | S | ||
| PERIMETER_MID_ZN_2_WALL_EAST_WINDOW | WINDOW NON-RES FIXED | 43.52 | 0.00 | 0.00 | 43.52 | 43.52 | 3.940 | 0.519 | 0.398 | No | PERIMETER_MID_ZN_2_WALL_EAST | 90.00 | 90.00 | E | ||
| PERIMETER_MID_ZN_3_WALL_NORTH_WINDOW | WINDOW NON-RES FIXED | 65.28 | 0.00 | 0.00 | 65.28 | 65.28 | 3.940 | 0.519 | 0.398 | No | PERIMETER_MID_ZN_3_WALL_NORTH | 0.00 | 90.00 | N | ||
| PERIMETER_MID_ZN_4_WALL_WEST_WINDOW | WINDOW NON-RES FIXED | 43.52 | 0.00 | 0.00 | 43.52 | 43.52 | 3.940 | 0.519 | 0.398 | No | PERIMETER_MID_ZN_4_WALL_WEST | 270.00 | 90.00 | W | ||
| PERIMETER_TOP_ZN_1_WALL_SOUTH_WINDOW | WINDOW NON-RES FIXED | 65.28 | 0.00 | 0.00 | 65.28 | 65.28 | 3.940 | 0.519 | 0.398 | No | PERIMETER_TOP_ZN_1_WALL_SOUTH | 180.00 | 90.00 | S | ||
| PERIMETER_TOP_ZN_2_WALL_EAST_WINDOW | WINDOW NON-RES FIXED | 43.52 | 0.00 | 0.00 | 43.52 | 43.52 | 3.940 | 0.519 | 0.398 | No | PERIMETER_TOP_ZN_2_WALL_EAST | 90.00 | 90.00 | E | ||
| PERIMETER_TOP_ZN_3_WALL_NORTH_WINDOW | WINDOW NON-RES FIXED | 65.28 | 0.00 | 0.00 | 65.28 | 65.28 | 3.940 | 0.519 | 0.398 | No | PERIMETER_TOP_ZN_3_WALL_NORTH | 0.00 | 90.00 | N | ||
| PERIMETER_TOP_ZN_4_WALL_WEST_WINDOW | WINDOW NON-RES FIXED | 43.52 | 0.00 | 0.00 | 43.52 | 43.52 | 3.940 | 0.519 | 0.398 | No | PERIMETER_TOP_ZN_4_WALL_WEST | 270.00 | 90.00 | W | ||
| Total or Average | 652.83 | 3.940 | 0.519 | 0.398 | ||||||||||||
| North Total or Average | 195.85 | 3.940 | 0.519 | 0.398 | ||||||||||||
| Non-North Total or Average | 456.98 | 3.940 | 0.519 | 0.398 |
| Construction | Area of One Opening [m2] | Area of Openings [m2] | Glass U-Factor [W/m2-K] | Glass SHGC | Glass Visible Transmittance | Parent Surface | |
| Total or Average | 0.00 | - | - | - |
| Construction | U-Factor with Film [W/m2-K] | U-Factor no Film [W/m2-K] | Gross Area [m2] | Parent Surface | |
| None |
Report: Equipment Summary
For: Entire Facility
Timestamp: 2021-07-16 12:27:08
Central Plant| Type | Nominal Capacity [W] | Nominal Efficiency [W/W] | IPLV in SI Units [W/W] | IPLV in IP Units [Btu/W-h] | |
| None |
| Type | Design Coil Load [W] | Nominal Total Capacity [W] | Nominal Sensible Capacity [W] | Nominal Latent Capacity [W] | Nominal Sensible Heat Ratio | Nominal Efficiency [W/W] | Nominal Coil UA Value [W/C] | Nominal Coil Surface Area [m2] | |
| VAV_1_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | 128747.62 | 98394.95 | 30352.67 | 0.76 | 3.23 | |||
| VAV_2_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | 128751.75 | 100078.36 | 28673.39 | 0.78 | 3.23 | |||
| VAV_3_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | 132932.33 | 103811.06 | 29121.27 | 0.78 | 3.23 |
| DX Cooling Coil Type | Standard Rated Net Cooling Capacity [W] | Standard Rated Net COP [W/W] | EER [Btu/W-h] | SEER [Btu/W-h] | IEER [Btu/W-h] | |
| VAV_1_COOLC DXCOIL | 127750.0 | 2.80 | 9.55 | N/A | 12.67 | |
| VAV_2_COOLC DXCOIL | 127754.2 | 2.79 | 9.51 | N/A | 12.63 | |
| VAV_3_COOLC DXCOIL | 131928.5 | 2.78 | 9.50 | N/A | 12.62 |
| DX Cooling Coil Type | Rated Net Cooling Capacity Test A [W] | Rated Electric Power Test A [W] | Rated Net Cooling Capacity Test B [W] | Rated Electric Power Test B [W] | Rated Net Cooling Capacity Test C [W] | Rated Electric Power Test C [W] | Rated Net Cooling Capacity Test D [W] | Rated Electric Power Test D [W] | |
| VAV_1_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | ||||||||
| VAV_2_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | ||||||||
| VAV_3_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed |
| DX Heating Coil Type | High Temperature Heating (net) Rating Capacity [W] | Low Temperature Heating (net) Rating Capacity [W] | HSPF [Btu/W-h] | Region Number | |
| None |
| Type | Design Coil Load [W] | Nominal Total Capacity [W] | Nominal Efficiency [W/W] | |
| CORE_BOTTOM VAV BOX REHEAT COIL | Coil:Heating:Electric | 30883.71 | 1.00 | |
| CORE_MID VAV BOX REHEAT COIL | Coil:Heating:Electric | 26948.43 | 1.00 | |
| CORE_TOP VAV BOX REHEAT COIL | Coil:Heating:Electric | 27634.54 | 1.00 | |
| PERIMETER_BOT_ZN_1 VAV BOX REHEAT COIL | Coil:Heating:Electric | 10861.75 | 1.00 | |
| PERIMETER_BOT_ZN_2 VAV BOX REHEAT COIL | Coil:Heating:Electric | 13725.23 | 1.00 | |
| PERIMETER_BOT_ZN_3 VAV BOX REHEAT COIL | Coil:Heating:Electric | 10613.78 | 1.00 | |
| PERIMETER_BOT_ZN_4 VAV BOX REHEAT COIL | Coil:Heating:Electric | 15280.08 | 1.00 | |
| PERIMETER_MID_ZN_1 VAV BOX REHEAT COIL | Coil:Heating:Electric | 12962.44 | 1.00 | |
| PERIMETER_MID_ZN_2 VAV BOX REHEAT COIL | Coil:Heating:Electric | 15214.99 | 1.00 | |
| PERIMETER_MID_ZN_3 VAV BOX REHEAT COIL | Coil:Heating:Electric | 12758.89 | 1.00 | |
| PERIMETER_MID_ZN_4 VAV BOX REHEAT COIL | Coil:Heating:Electric | 16481.15 | 1.00 | |
| PERIMETER_TOP_ZN_1 VAV BOX REHEAT COIL | Coil:Heating:Electric | 16732.78 | 1.00 | |
| PERIMETER_TOP_ZN_2 VAV BOX REHEAT COIL | Coil:Heating:Electric | 15201.84 | 1.00 | |
| PERIMETER_TOP_ZN_3 VAV BOX REHEAT COIL | Coil:Heating:Electric | 16557.24 | 1.00 | |
| PERIMETER_TOP_ZN_4 VAV BOX REHEAT COIL | Coil:Heating:Electric | 17360.91 | 1.00 | |
| VAV_1_HEATC | Coil:Heating:Fuel | 38216.32 | 0.80 | |
| VAV_2_HEATC | Coil:Heating:Fuel | 38030.07 | 0.80 | |
| VAV_3_HEATC | Coil:Heating:Fuel | 37794.37 | 0.80 |
| Type | Total Efficiency [W/W] | Delta Pressure [pa] | Max Air Flow Rate [m3/s] | Rated Electric Power [W] | Rated Power Per Max Air Flow Rate [W-s/m3] | Motor Heat In Air Fraction | Fan Energy Index | End Use Subcategory | Design Day Name for Fan Sizing Peak | Date/Time for Fan Sizing Peak | |
| VAV_1_FAN | Fan:SystemModel | 0.59 | 1109.65 | 6.31 | 11836.23 | 1875.99 | 1.00 | 1.13 | Fan Energy | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 18:00:00 |
| VAV_2_FAN | Fan:VariableVolume | 0.59 | 1109.65 | 6.52 | 12235.48 | 1875.99 | 1.00 | 1.12 | Fan Energy | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 16:30:00 |
| VAV_3_FAN | Fan:VariableVolume | 0.59 | 1109.65 | 6.79 | 12740.72 | 1875.99 | 1.00 | 1.12 | Fan Energy | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 16:50:00 |
| Type | Control | Head [pa] | Water Flow [m3/s] | Electric Power [W] | Power Per Water Flow Rate [W-s/m3] | Motor Efficiency [W/W] | End Use Subcategory | |
| SWHSYS1 PUMP | Pump:ConstantSpeed | Intermittent | 179352.00 | 0.000031 | 8.44 | 270515.84 | 0.85 | General |
| Type | Storage Volume [m3] | Input [W] | Thermal Efficiency [W/W] | Recovery Efficiency [W/W] | Energy Factor | |
| SWHSYS1 WATER HEATER | WaterHeater:Mixed | 0.38 | 845000.00 | 0.80 | 1.83 | 0.57 |
| DX Cooling Coil Type | Assocated Fan | Net Cooling Capacity [W] | COP [W/W] | EER [Btu/W-h] | IEER [Btu/W-h] | Supply Air Flow 100% [kg/s] | COP 75% Capacity [W/W] | EER 75% Capacity [Btu/W-h] | Supply Air Flow 75% [kg/s] | COP 50% Capacity [W/W] | EER 50% Capacity [Btu/W-h] | Supply Air Flow 50% [kg/s] | COP 25% Capacity [W/W] | EER 25% Capacity [Btu/W-h] | Supply Air Flow 25% [kg/s] | |
| VAV_1_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | VAV_1_FAN | 127750.04 | 2.80 | 9.55 | 12.67 | 7.2183 | 3.46 | 11.79 | 5.8267 | 4.16 | 14.18 | 3.9068 | 4.28 | 14.60 | 1.5903 |
| VAV_2_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | VAV_2_FAN | 127754.17 | 2.79 | 9.51 | 12.63 | 7.4618 | 3.44 | 11.75 | 6.0292 | 4.15 | 14.14 | 4.0443 | 4.27 | 14.58 | 1.6284 |
| VAV_3_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | VAV_3_FAN | 131928.53 | 2.78 | 9.50 | 12.62 | 7.7699 | 3.44 | 11.74 | 6.2814 | 4.14 | 14.14 | 4.2138 | 4.27 | 14.58 | 1.6910 |
Report: HVAC Sizing Summary
For: Entire Facility
Timestamp: 2021-07-16 12:27:08
Zone Sensible Cooling| Calculated Design Load [W] | User Design Load [W] | User Design Load per Area [W/m2] | Calculated Design Air Flow [m3/s] | User Design Air Flow [m3/s] | Design Day Name | Date/Time Of Peak {TIMESTAMP} | Thermostat Setpoint Temperature at Peak Load [C] | Indoor Temperature at Peak Load [C] | Indoor Humidity Ratio at Peak Load [kgWater/kgAir] | Outdoor Temperature at Peak Load [C] | Outdoor Humidity Ratio at Peak Load [kgWater/kgAir] | Minimum Outdoor Air Flow Rate [m3/s] | Heat Gain Rate from DOAS [W] | |
| CORE_BOTTOM | 24918.66 | 33141.82 | 33.70 | 1.914 | 2.545 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 09:00:00 | 23.51 | 23.51 | 0.00888 | 25.31 | 0.01878 | 0.545 | 0.00 |
| CORE_MID | 22475.45 | 29892.34 | 30.39 | 1.670 | 2.221 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 07:00:00 | 23.51 | 23.51 | 0.00894 | 21.46 | 0.01650 | 0.545 | 0.00 |
| CORE_TOP | 23020.69 | 30617.51 | 31.13 | 1.712 | 2.277 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 07:00:00 | 23.51 | 23.51 | 0.00889 | 21.46 | 0.01650 | 0.545 | 0.00 |
| PERIMETER_BOT_ZN_1 | 8344.37 | 11098.01 | 53.53 | 0.641 | 0.852 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 13:10:00 | 23.51 | 23.51 | 0.00873 | 30.75 | 0.01878 | 0.115 | 0.00 |
| PERIMETER_BOT_ZN_2 | 11075.39 | 14730.27 | 112.22 | 0.850 | 1.131 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 08:50:00 | 23.51 | 23.51 | 0.00861 | 24.98 | 0.01878 | 0.073 | 0.00 |
| PERIMETER_BOT_ZN_3 | 5110.54 | 6797.02 | 32.78 | 0.392 | 0.522 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 15:00:00 | 23.51 | 23.51 | 0.00887 | 31.20 | 0.01878 | 0.115 | 0.00 |
| PERIMETER_BOT_ZN_4 | 12330.07 | 16399.00 | 124.94 | 0.947 | 1.259 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 17:10:00 | 23.51 | 23.51 | 0.00860 | 29.52 | 0.01878 | 0.073 | 0.00 |
| PERIMETER_MID_ZN_1 | 9921.72 | 13195.88 | 63.64 | 0.762 | 1.013 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 13:10:00 | 23.51 | 23.51 | 0.00869 | 30.75 | 0.01878 | 0.115 | 0.00 |
| PERIMETER_MID_ZN_2 | 12277.56 | 16329.16 | 124.40 | 0.943 | 1.254 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 08:40:00 | 23.51 | 23.51 | 0.00860 | 24.64 | 0.01878 | 0.073 | 0.00 |
| PERIMETER_MID_ZN_3 | 6621.40 | 8806.46 | 42.47 | 0.508 | 0.676 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 15:00:00 | 23.51 | 23.51 | 0.00879 | 31.20 | 0.01878 | 0.115 | 0.00 |
| PERIMETER_MID_ZN_4 | 13299.29 | 17688.06 | 134.76 | 1.021 | 1.358 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 17:10:00 | 23.51 | 23.51 | 0.00859 | 29.52 | 0.01878 | 0.073 | 0.00 |
| PERIMETER_TOP_ZN_1 | 10406.67 | 13840.87 | 66.76 | 0.799 | 1.063 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 13:20:00 | 23.51 | 23.51 | 0.00868 | 30.84 | 0.01878 | 0.115 | 0.00 |
| PERIMETER_TOP_ZN_2 | 12266.95 | 16315.04 | 124.29 | 0.942 | 1.253 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 08:40:00 | 23.51 | 23.51 | 0.00860 | 24.64 | 0.01878 | 0.073 | 0.00 |
| PERIMETER_TOP_ZN_3 | 7520.73 | 10002.57 | 48.24 | 0.577 | 0.768 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 15:40:00 | 23.51 | 23.51 | 0.00875 | 30.77 | 0.01878 | 0.115 | 0.00 |
| PERIMETER_TOP_ZN_4 | 14009.22 | 18632.27 | 141.96 | 1.076 | 1.431 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 17:10:00 | 23.51 | 23.51 | 0.00859 | 29.52 | 0.01878 | 0.073 | 0.00 |
| Calculated Design Load [W] | User Design Load [W] | User Design Load per Area [W/m2] | Calculated Design Air Flow [m3/s] | User Design Air Flow [m3/s] | Design Day Name | Date/Time Of Peak {TIMESTAMP} | Thermostat Setpoint Temperature at Peak Load [C] | Indoor Temperature at Peak Load [C] | Indoor Humidity Ratio at Peak Load [kgWater/kgAir] | Outdoor Temperature at Peak Load [C] | Outdoor Humidity Ratio at Peak Load [kgWater/kgAir] | Minimum Outdoor Air Flow Rate [m3/s] | Heat Gain Rate from DOAS [W] | |
| CORE_BOTTOM | 3131.24 | 4164.55 | 4.23 | 0.084 | 0.545 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 19.14 | 19.14 | 0.00799 | -20.60 | 0.00061 | 0.545 | 0.00 |
| CORE_MID | 7413.07 | 9859.39 | 10.02 | 0.198 | 0.545 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:10:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.545 | 0.00 |
| CORE_TOP | 18443.01 | 24529.20 | 24.94 | 0.492 | 0.654 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.545 | 0.00 |
| PERIMETER_BOT_ZN_1 | 7569.06 | 10066.85 | 48.55 | 0.202 | 0.269 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.115 | 0.00 |
| PERIMETER_BOT_ZN_2 | 5047.76 | 6713.52 | 51.15 | 0.135 | 0.179 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.073 | 0.00 |
| PERIMETER_BOT_ZN_3 | 7396.26 | 9837.03 | 47.44 | 0.197 | 0.262 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.115 | 0.00 |
| PERIMETER_BOT_ZN_4 | 5034.10 | 6695.36 | 51.01 | 0.134 | 0.179 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.073 | 0.00 |
| PERIMETER_MID_ZN_1 | 9032.94 | 12013.81 | 57.94 | 0.241 | 0.320 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:20:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.115 | 0.00 |
| PERIMETER_MID_ZN_2 | 5940.97 | 7901.48 | 60.20 | 0.158 | 0.211 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:30:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.073 | 0.00 |
| PERIMETER_MID_ZN_3 | 8891.09 | 11825.15 | 57.03 | 0.237 | 0.315 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:20:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.115 | 0.00 |
| PERIMETER_MID_ZN_4 | 5984.94 | 7959.97 | 60.65 | 0.160 | 0.212 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:30:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.073 | 0.00 |
| PERIMETER_TOP_ZN_1 | 11660.31 | 15508.22 | 74.80 | 0.311 | 0.414 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.115 | 0.00 |
| PERIMETER_TOP_ZN_2 | 7575.89 | 10075.93 | 76.76 | 0.202 | 0.269 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.073 | 0.00 |
| PERIMETER_TOP_ZN_3 | 11537.99 | 15345.53 | 74.01 | 0.308 | 0.409 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.115 | 0.00 |
| PERIMETER_TOP_ZN_4 | 7652.37 | 10177.65 | 77.54 | 0.204 | 0.271 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 19.14 | 19.14 | 0.00800 | -20.60 | 0.00061 | 0.073 | 0.00 |
| Calculated cooling [m3/s] | User cooling [m3/s] | Calculated heating [m3/s] | User heating [m3/s] | Adjusted cooling [m3/s] | Adjusted heating [m3/s] | Adjusted main [m3/s] | Calculated Heating Air Flow Ratio [] | User Heating Air Flow Ratio [] | |
| VAV_1 | 6.31 | 6.31 | 1.43 | 1.43 | 6.3093 | 1.8928 | 6.3093 | 0.3000 | 0.3000 |
| VAV_2 | 6.52 | 6.52 | 1.60 | 1.60 | 6.5221 | 1.9566 | 6.5221 | 0.3000 | 0.3000 |
| VAV_3 | 6.79 | 6.79 | 2.02 | 2.02 | 6.7915 | 2.0374 | 6.7915 | 0.3000 | 0.3000 |
| Previous Design Volume Flow Rate [m3/s] | Algorithm Volume Flow Rate [m3/s] | Coincident Design Volume Flow Rate [m3/s] | Coincident Size Adjusted | Peak Sizing Period Name | Peak Day into Period {TIMESTAMP}[day] | Peak Hour Of Day {TIMESTAMP}[hr] | Peak Step Start Minute {TIMESTAMP}[min] | |
| None |
| Coil Type | HVAC Type | HVAC Name | Coil Final Gross Total Capacity [W] | Coil Final Gross Sensible Capacity [W] | Coil Final Reference Air Volume Flow Rate [m3/s] | Coil Final Reference Plant Fluid Volume Flow Rate [m3/s] | Coil U-value Times Area Value [W/K] | Design Day Name at Sensible Ideal Loads Peak | Date/Time at Sensible Ideal Loads Peak | Design Day Name at Air Flow Ideal Loads Peak | Date/Time at Air Flow Ideal Loads Peak | Coil Total Capacity at Ideal Loads Peak [W] | Coil Sensible Capacity at Ideal Loads Peak [W] | Coil Air Volume Flow Rate at Ideal Loads Peak [m3/s] | Coil Entering Air Drybulb at Ideal Loads Peak [C] | Coil Entering Air Wetbulb at Ideal Loads Peak [C] | Coil Entering Air Humidity Ratio at Ideal Loads Peak [KGWATER/KGDRYAIR] | Coil Leaving Air Drybulb at Ideal Loads Peak [C] | Coil Leaving Air Wetbulb at Ideal Loads Peak [C] | Coil Leaving Air Humidity Ratio at Ideal Loads Peak [C] | Outdoor Air Drybulb at Ideal Loads Peak [C] | Outdoor Air Humidity Ratio at Ideal Loads Peak [KGWATER/KGDRYAIR] | Outdoor Air Wetbulb at Ideal Loads Peak [C] | Outdoor Air Flow Percentage at Ideal Loads Peak [%] | Zone Air Drybulb at Ideal Loads Peak [C] | Zone Air Humidity Ratio at Ideal Loads Peak [KGWATER/KGDRYAIR] | Zone Air Relative Humidity at Ideal Loads Peak [%] | Zone Sensible Heat Gain at Ideal Loads Peak [W] | Zone Latent Heat Gain at Ideal Loads Peak [W] | Coil Total Capacity at Rating Conditions [W] | Coil Sensible Capacity at Rating Conditions [W] | |
| CORE_BOTTOM VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | CORE_BOTTOM VAV BOX | 30883.708 | 30883.708 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 30883.71 | 22027.79 | 0.544570 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00799482 | 57.3800 | 4164.55 | 0.00 | -999.00 | -999.00 |
| CORE_MID VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | CORE_MID VAV BOX | 26948.427 | 26948.427 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:10:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:10:00 | 26948.43 | 22027.79 | 0.544570 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 9859.39 | 0.00 | -999.00 | -999.00 |
| CORE_TOP VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | CORE_TOP VAV BOX | 27634.542 | 27634.542 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 27634.54 | 26466.07 | 0.654293 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 24529.20 | 0.00 | -999.00 | -999.00 |
| PERIMETER_BOT_ZN_1 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_BOT_ZN_1 VAV BOX | 10861.749 | 10861.749 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 10861.75 | 10861.75 | 0.268524 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 10066.85 | 0.00 | -999.00 | -999.00 |
| PERIMETER_BOT_ZN_2 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_BOT_ZN_2 VAV BOX | 13725.228 | 13725.228 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 13725.23 | 7243.63 | 0.179077 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 6713.52 | 0.00 | -999.00 | -999.00 |
| PERIMETER_BOT_ZN_3 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_BOT_ZN_3 VAV BOX | 10613.779 | 10613.779 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 10613.78 | 10613.78 | 0.262393 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 9837.03 | 0.00 | -999.00 | -999.00 |
| PERIMETER_BOT_ZN_4 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_BOT_ZN_4 VAV BOX | 15280.079 | 15280.079 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 15280.08 | 7224.04 | 0.178592 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 6695.36 | 0.00 | -999.00 | -999.00 |
| PERIMETER_MID_ZN_1 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_MID_ZN_1 VAV BOX | 12962.442 | 12962.442 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:20:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:20:00 | 12962.44 | 12962.44 | 0.320457 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 12013.81 | 0.00 | -999.00 | -999.00 |
| PERIMETER_MID_ZN_2 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_MID_ZN_2 VAV BOX | 15214.992 | 15214.992 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:30:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:30:00 | 15214.99 | 8525.40 | 0.210764 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 7901.48 | 0.00 | -999.00 | -999.00 |
| PERIMETER_MID_ZN_3 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_MID_ZN_3 VAV BOX | 12758.887 | 12758.887 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:20:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:20:00 | 12758.89 | 12758.89 | 0.315424 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 11825.15 | 0.00 | -999.00 | -999.00 |
| PERIMETER_MID_ZN_4 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_MID_ZN_4 VAV BOX | 16481.153 | 16481.153 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:30:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:30:00 | 16481.15 | 8588.50 | 0.212325 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 7959.97 | 0.00 | -999.00 | -999.00 |
| PERIMETER_TOP_ZN_1 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_TOP_ZN_1 VAV BOX | 16732.775 | 16732.775 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 16732.78 | 16732.78 | 0.413667 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 15508.22 | 0.00 | -999.00 | -999.00 |
| PERIMETER_TOP_ZN_2 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_TOP_ZN_2 VAV BOX | 15201.836 | 15201.836 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 15201.84 | 10871.55 | 0.268766 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 10075.93 | 0.00 | -999.00 | -999.00 |
| PERIMETER_TOP_ZN_3 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_TOP_ZN_3 VAV BOX | 16557.242 | 16557.242 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 16557.24 | 16557.24 | 0.409327 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 15345.53 | 0.00 | -999.00 | -999.00 |
| PERIMETER_TOP_ZN_4 VAV BOX REHEAT COIL | Coil:Heating:Electric | ZONEHVAC:AIRDISTRIBUTIONUNIT | PERIMETER_TOP_ZN_4 VAV BOX | 17360.907 | 17360.907 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 17360.91 | 10981.30 | 0.271479 | 16.70 | 13.08 | 0.00800000 | 50.00 | 23.81 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 0.0000 | 19.14 | 0.00800000 | 57.4167 | 10177.65 | 0.00 | -999.00 | -999.00 |
| VAV_1_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | AirLoopHVAC | VAV_1 | 128747.619 | 98394.951 | 6.309326 | -999.0 | -999.000 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 18:00:00 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 18:00:00 | 127361.41 | 95806.89 | 6.309326 | 24.43 | 17.84 | 0.01021584 | 11.26 | 11.26 | 0.00850000 | 27.32 | 0.01878287 | 24.67 | 14.5740 | 23.51 | 0.00875427 | 47.9906 | 82166.12 | 0.00 | 128735.06 | 98306.62 |
| VAV_1_HEATC | Coil:Heating:Fuel | AirLoopHVAC | VAV_1 | 38216.316 | 38216.316 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 13:20:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 07:00:00 | 38216.32 | 38216.32 | 1.892798 | -6.36 | -6.36 | 0.00325948 | 16.70 | 13.08 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 48.5800 | 19.14 | 0.00799803 | 57.4027 | 37477.30 | 0.00 | -999.00 | -999.00 |
| VAV_2_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | AirLoopHVAC | VAV_2 | 128751.750 | 100078.363 | 6.522148 | -999.0 | -999.000 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 16:00:00 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 16:30:00 | 127061.28 | 97658.20 | 6.522148 | 24.30 | 17.66 | 0.01005260 | 11.26 | 11.26 | 0.00850000 | 26.20 | 0.01800401 | 23.90 | 14.0984 | 23.51 | 0.00874758 | 47.9539 | 85911.91 | 0.00 | 128739.19 | 99967.12 |
| VAV_2_HEATC | Coil:Heating:Fuel | AirLoopHVAC | VAV_2 | 38030.066 | 38030.066 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 17:20:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 09:20:00 | 38030.07 | 38030.07 | 1.956644 | -3.65 | -3.65 | 0.00376381 | 16.70 | 13.08 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 46.9948 | 19.14 | 0.00800000 | 57.4167 | 49559.80 | 0.00 | -999.00 | -999.00 |
| VAV_3_COOLC DXCOIL | Coil:Cooling:DX:TwoSpeed | AirLoopHVAC | VAV_3 | 132932.329 | 103811.064 | 6.791467 | -999.0 | -999.000 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 16:40:00 | CHICAGO ANN CLG .4% CONDNS WB=>MDB | 7/21 16:50:00 | 131529.24 | 102126.17 | 6.791467 | 24.28 | 17.60 | 0.00998285 | 11.26 | 11.26 | 0.00850000 | 26.27 | 0.01801585 | 23.92 | 13.5394 | 23.51 | 0.00872491 | 47.8311 | 89408.26 | 0.00 | 132919.36 | 103680.70 |
| VAV_3_HEATC | Coil:Heating:Fuel | AirLoopHVAC | VAV_3 | 37794.374 | 37794.374 | -999.0 | -999.0 | -999.000 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 01:00:00 | CHICAGO ANN HTG 99.6% CONDNS DB | 1/21 24:00:00 | 37794.37 | 37794.37 | 2.037440 | 1.03 | 1.03 | 0.00463307 | 16.70 | 13.08 | 0.00800000 | -20.60 | 0.00061258 | -20.60 | 45.1312 | 19.14 | 0.00800000 | 57.4167 | 75636.53 | 0.00 | -999.00 | -999.00 |
Report: System Summary
For: Entire Facility
Timestamp: 2021-07-16 12:27:08
Economizer| High Limit Shutoff Control | Minimum Outdoor Air [m3/s] | Maximum Outdoor Air [m3/s] | Return Air Temp Limit | Return Air Enthalpy Limit | Outdoor Air Temperature Limit [C] | Outdoor Air Enthalpy Limit [C] | |
| VAV_1_OA_CONTROLLER | DifferentialDryBulb | 0.92 | 6.31 | - | |||
| VAV_2_OA_CONTROLLER | DifferentialDryBulb | 0.92 | 6.52 | - | |||
| VAV_3_OA_CONTROLLER | DifferentialDryBulb | 0.92 | 6.79 | - |
| Controller:MechanicalVentilation Name | Outdoor Air Per Person [m3/s-person] | Outdoor Air Per Area [m3/s-m2] | Outdoor Air Per Zone [m3/s] | Outdoor Air ACH [ach] | Outdoor Air Method | Outdoor Air Schedule Name | Air Distribution Effectiveness in Cooling Mode | Air Distribution Effectiveness in Heating Mode | Air Distribution Effectiveness Schedule Name | |
| None |
| Winter Clothes [hr] | Summer Clothes [hr] | Summer or Winter Clothes [hr] | |
| CORE_BOTTOM | 1040.17 | 4662.67 | 1040.17 |
| CORE_MID | 1985.50 | 4367.33 | 1919.17 |
| CORE_TOP | 2534.83 | 4081.33 | 2265.83 |
| PERIMETER_BOT_ZN_1 | 2258.67 | 3705.67 | 1460.33 |
| PERIMETER_BOT_ZN_2 | 2912.33 | 4393.33 | 2432.83 |
| PERIMETER_BOT_ZN_3 | 2693.00 | 4312.33 | 2490.17 |
| PERIMETER_BOT_ZN_4 | 3065.33 | 4470.50 | 2577.50 |
| PERIMETER_MID_ZN_1 | 2434.67 | 3520.50 | 1420.00 |
| PERIMETER_MID_ZN_2 | 3011.50 | 4199.83 | 2387.67 |
| PERIMETER_MID_ZN_3 | 2983.17 | 4030.67 | 2454.17 |
| PERIMETER_MID_ZN_4 | 3062.17 | 4341.17 | 2482.50 |
| PERIMETER_TOP_ZN_1 | 2685.67 | 3530.83 | 1602.67 |
| PERIMETER_TOP_ZN_2 | 3208.00 | 4101.67 | 2487.83 |
| PERIMETER_TOP_ZN_3 | 3291.50 | 3991.50 | 2617.67 |
| PERIMETER_TOP_ZN_4 | 3241.00 | 4301.83 | 2606.00 |
| FIRSTFLOOR_PLENUM | 0.00 | 0.00 | 0.00 |
| MIDFLOOR_PLENUM | 0.00 | 0.00 | 0.00 |
| TOPFLOOR_PLENUM | 0.00 | 0.00 | 0.00 |
| Facility | 4537.83 | 5030.50 | 3050.17 |
| During Heating [hr] | During Cooling [hr] | During Occupied Heating [hr] | During Occupied Cooling [hr] | |
| CORE_BOTTOM | 0.00 | 0.00 | 0.00 | 0.00 |
| CORE_MID | 0.50 | 0.00 | 0.50 | 0.00 |
| CORE_TOP | 9.00 | 0.00 | 9.00 | 0.00 |
| PERIMETER_BOT_ZN_1 | 4.50 | 17.33 | 3.50 | 5.50 |
| PERIMETER_BOT_ZN_2 | 6.00 | 5.50 | 0.00 | 0.00 |
| PERIMETER_BOT_ZN_3 | 7.83 | 0.00 | 4.33 | 0.00 |
| PERIMETER_BOT_ZN_4 | 4.17 | 11.33 | 0.00 | 0.00 |
| PERIMETER_MID_ZN_1 | 7.50 | 15.33 | 1.17 | 1.33 |
| PERIMETER_MID_ZN_2 | 31.83 | 10.33 | 0.33 | 0.00 |
| PERIMETER_MID_ZN_3 | 33.17 | 0.00 | 2.17 | 0.00 |
| PERIMETER_MID_ZN_4 | 26.17 | 15.33 | 0.17 | 0.00 |
| PERIMETER_TOP_ZN_1 | 56.83 | 12.17 | 1.83 | 0.00 |
| PERIMETER_TOP_ZN_2 | 114.67 | 9.50 | 3.67 | 0.00 |
| PERIMETER_TOP_ZN_3 | 127.50 | 0.00 | 5.83 | 0.00 |
| PERIMETER_TOP_ZN_4 | 105.00 | 18.83 | 1.67 | 0.00 |
| FIRSTFLOOR_PLENUM | 0.00 | 0.00 | 0.00 | 0.00 |
| MIDFLOOR_PLENUM | 0.00 | 0.00 | 0.00 | 0.00 |
| TOPFLOOR_PLENUM | 0.00 | 0.00 | 0.00 | 0.00 |
| Facility | 170.67 | 90.17 | 13.33 | 5.50 |
Report: EMISSIONS DATA SUMMARY
For: Meter
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| CO2:FACILITY [kg] | NOX:FACILITY [kg] | SO2:FACILITY [kg] | PM:FACILITY [kg] | HG:FACILITY [kg] | WATERENVIRONMENTALFACTORS:FACILITY [L] | CARBON EQUIVALENT:FACILITY [kg] | |
| January | 63339.8588 | 113.3134 | 334.8861 | 0.0000 | 0.0010 | 77091.9481 | 18231.0634 |
| February | 48804.3071 | 87.6669 | 260.1763 | 0.0000 | 0.0008 | 59893.9813 | 14052.7232 |
| March | 44484.5839 | 80.2219 | 239.0329 | 0.0000 | 0.0007 | 55027.0791 | 12813.6689 |
| April | 37557.6548 | 68.2170 | 204.7306 | 0.0000 | 0.0006 | 47131.0759 | 10825.7670 |
| May | 41556.4630 | 75.5247 | 226.7956 | 0.0000 | 0.0007 | 52210.7223 | 11979.0748 |
| June | 48022.1381 | 87.3128 | 262.3062 | 0.0000 | 0.0008 | 60385.6936 | 13843.4383 |
| July | 51259.8802 | 93.2165 | 280.0932 | 0.0000 | 0.0008 | 64480.4677 | 14777.0462 |
| August | 50138.5893 | 91.1721 | 273.9342 | 0.0000 | 0.0008 | 63062.5862 | 14453.7226 |
| September | 42373.3727 | 77.0348 | 231.4069 | 0.0000 | 0.0007 | 53272.3345 | 12214.9444 |
| October | 42939.5412 | 78.0248 | 234.2633 | 0.0000 | 0.0007 | 53929.8508 | 12377.5576 |
| November | 43071.9899 | 77.9764 | 233.2524 | 0.0000 | 0.0007 | 53696.7573 | 12411.3524 |
| December | 53139.8297 | 95.8670 | 285.7603 | 0.0000 | 0.0009 | 65784.0989 | 15307.3448 |
| Annual Sum or Average |<PHONE_NUMBER> | 1025.5482 | 3066.6381 | 0.0000 | 0.0093 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| Minimum of Months | 37557.6548 | 68.2170 | 204.7306 | 0.0000 | 0.0006 | 47131.0759 | 10825.7670 |
| Maximum of Months | 63339.8588 | 113.3134 | 334.8861 | 0.0000 | 0.0010 | 77091.9481 | 18231.0634 |
Report: COMPONENTS OF PEAK ELECTRICAL DEMAND
For: Meter
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| ELECTRICITY:FACILITY [J] | ELECTRICITY:FACILITY {Maximum}[W] | ELECTRICITY:FACILITY {TIMESTAMP} | INTERIORLIGHTS:ELECTRICITY {AT MAX/MIN} [W] | INTERIOREQUIPMENT:ELECTRICITY {AT MAX/MIN} [W] | FANS:ELECTRICITY {AT MAX/MIN} [W] | HEATING:ELECTRICITY {AT MAX/MIN} [W] | COOLING:ELECTRICITY {AT MAX/MIN} [W] | EXTERIORLIGHTS:ELECTRICITY {AT MAX/MIN} [W] | PUMPS:ELECTRICITY {AT MAX/MIN} [W] | HEATREJECTION:ELECTRICITY [] | EXTERIOREQUIPMENT:ELECTRICITY [] | HUMIDIFICATION:ELECTRICITY [] | HEATRECOVERY:ELECTRICITY [] | WATERSYSTEMS:ELECTRICITY [] | REFRIGERATION:ELECTRICITY [] | GENERATORS:ELECTRICITY [] | ELECTRICITYPRODUCED:FACILITY [] | |
| January | 0.178886E+12 | 226642.607 | 04-JAN-06:10 | 1859.352 | 10201.524 | 3169.220 | 196600.072 | 0.000 | 14804.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| February | 0.138979E+12 | 215661.819 | 10-FEB-06:10 | 1859.352 | 10201.524 | 3169.220 | 185619.283 | 0.000 | 14804.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| March | 0.127686E+12 | 202540.983 | 04-MAR-06:10 | 1859.352 | 10201.524 | 3169.220 | 172498.447 | 0.000 | 14804.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| April | 0.109364E+12 | 108381.425 | 01-APR-05:10 | 1859.352 | 10201.524 | 3169.220 | 78338.890 | 0.000 | 14804.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| May | 0.121151E+12 | 107586.489 | 02-MAY-12:00 | 16734.166 | 38366.176 | 4677.883 | 0.000 | 47799.823 | 0.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| June | 0.140120E+12 | 128423.174 | 17-JUN-15:00 | 16734.166 | 37723.978 | 7135.932 | 0.000 | 66820.658 | 0.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| July | 0.149622E+12 | 146248.149 | 02-JUL-15:09 | 16734.166 | 37723.978 | 7441.618 | 0.000 | 84339.947 | 0.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| August | 0.146332E+12 | 117876.546 | 22-AUG-12:20 | 16734.166 | 38366.176 | 4747.053 | 0.000 | 58020.711 | 0.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| September | 0.123614E+12 | 132014.557 | 11-SEP-12:00 | 16734.166 | 38366.176 | 6108.306 | 0.000 | 70797.469 | 0.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| October | 0.125140E+12 | 123671.697 | 28-OCT-05:10 | 1859.352 | 10201.524 | 3169.220 | 93629.162 | 0.000 | 14804.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| November | 0.124599E+12 | 197017.223 | 25-NOV-06:10 | 1859.352 | 10201.524 | 3169.220 | 166974.687 | 0.000 | 14804.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| December | 0.152647E+12 | 217718.461 | 09-DEC-06:10 | 1859.352 | 10201.524 | 3169.220 | 187675.926 | 0.000 | 14804.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Annual Sum or Average | 0.163814E+13 | |||||||||||||||||
| Minimum of Months | 0.109364E+12 | 107586.489 | 1859.352 | 10201.524 | 3169.220 | 0.000 | 0.000 | 0.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| Maximum of Months | 0.178886E+12 | 226642.607 | 16734.166 | 38366.176 | 7441.618 | 196600.072 | 84339.947 | 14804.000 | 8.440 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Report: HEATING PART LOAD PERFORMANCE
For: VAV_1_HEATC
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| HEATING COIL GAS RATE [W] | HEATING COIL GAS RATE {Maximum}[W] | HEATING COIL GAS RATE {TIMESTAMP} | |
| January | 4264. | 31787. | 04-JAN-07:10 |
| February | 2637. | 27914. | 12-FEB-07:10 |
| March | 1381. | 22258. | 01-MAR-07:10 |
| April | 26. | 5368. | 16-APR-06:03 |
| May | 0. | 0. | 01-MAY-00:10 |
| June | 0. | 0. | 01-JUN-00:10 |
| July | 0. | 0. | 01-JUL-00:10 |
| August | 0. | 0. | 01-AUG-00:10 |
| September | 0. | 0. | 01-SEP-00:10 |
| October | 44. | 7658. | 26-OCT-06:10 |
| November | 667. | 14392. | 25-NOV-07:10 |
| December | 1600. | 18812. | 11-DEC-07:05 |
| Annual Sum or Average | 890. | ||
| Minimum of Months | 0. | 0. | |
| Maximum of Months | 4264. | 31787. |
Report: HEATING PART LOAD PERFORMANCE
For: VAV_2_HEATC
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| HEATING COIL GAS RATE [W] | HEATING COIL GAS RATE {Maximum}[W] | HEATING COIL GAS RATE {TIMESTAMP} | |
| January | 4581. | 32008. | 04-JAN-07:10 |
| February | 2923. | 28033. | 12-FEB-07:10 |
| March | 1504. | 22283. | 01-MAR-07:10 |
| April | 19. | 5058. | 01-APR-06:03 |
| May | 0. | 0. | 01-MAY-00:10 |
| June | 0. | 0. | 01-JUN-00:10 |
| July | 0. | 0. | 01-JUL-00:10 |
| August | 0. | 0. | 01-AUG-00:10 |
| September | 0. | 0. | 01-SEP-00:10 |
| October | 42. | 7458. | 26-OCT-06:10 |
| November | 783. | 14832. | 25-NOV-07:10 |
| December | 1848. | 18981. | 11-DEC-07:05 |
| Annual Sum or Average | 980. | ||
| Minimum of Months | 0. | 0. | |
| Maximum of Months | 4581. | 32008. |
Report: HEATING PART LOAD PERFORMANCE
For: VAV_3_HEATC
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| HEATING COIL GAS RATE [W] | HEATING COIL GAS RATE {Maximum}[W] | HEATING COIL GAS RATE {TIMESTAMP} | |
| January | 5262. | 38447. | 04-JAN-07:10 |
| February | 3556. | 33770. | 12-FEB-07:10 |
| March | 1938. | 28123. | 01-MAR-07:10 |
| April | 71. | 8070. | 01-APR-06:03 |
| May | 0. | 0. | 01-MAY-00:10 |
| June | 0. | 0. | 01-JUN-00:10 |
| July | 0. | 0. | 01-JUL-00:10 |
| August | 0. | 0. | 01-AUG-00:10 |
| September | 1. | 2052. | 18-SEP-06:03 |
| October | 103. | 12041. | 26-OCT-06:05 |
| November | 1120. | 19398. | 25-NOV-07:10 |
| December | 2353. | 23671. | 11-DEC-07:05 |
| Annual Sum or Average | 1205. | ||
| Minimum of Months | 0. | 0. | |
| Maximum of Months | 5262. | 38447. |
Report: COOLING PART LOAD PERFORMANCE
For: VAV_1_COOLC DXCOIL
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| COOLING COIL ELECTRIC POWER [W] | COOLING COIL ELECTRIC POWER {Maximum}[W] | COOLING COIL ELECTRIC POWER {TIMESTAMP} | |
| January | 47. | 5674. | 11-JAN-15:00 |
| February | 0. | 0. | 01-FEB-00:10 |
| March | 155. | 4687. | 22-MAR-15:00 |
| April | 1159. | 9676. | 14-APR-13:00 |
| May | 3644. | 15662. | 02-MAY-14:00 |
| June | 6505. | 21453. | 17-JUN-16:10 |
| July | 7596. | 27148. | 02-JUL-15:10 |
| August | 6710. | 21493. | 29-AUG-11:30 |
| September | 3928. | 23563. | 11-SEP-13:00 |
| October | 2038. | 17220. | 07-OCT-11:10 |
| November | 357. | 8281. | 01-NOV-07:00 |
| December | 115. | 7643. | 22-DEC-12:22 |
| Annual Sum or Average | 2686. | ||
| Minimum of Months | 0. | 0. | |
| Maximum of Months | 7596. | 27148. |
Report: COOLING PART LOAD PERFORMANCE
For: VAV_2_COOLC DXCOIL
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| COOLING COIL ELECTRIC POWER [W] | COOLING COIL ELECTRIC POWER {Maximum}[W] | COOLING COIL ELECTRIC POWER {TIMESTAMP} | |
| January | 48. | 5872. | 11-JAN-15:00 |
| February | 0. | 0. | 01-FEB-00:10 |
| March | 157. | 4789. | 22-MAR-15:00 |
| April | 1203. | 10064. | 14-APR-15:00 |
| May | 3828. | 16315. | 02-MAY-14:00 |
| June | 6556. | 28067. | 19-JUN-05:02 |
| July | 7601. | 31159. | 14-JUL-05:02 |
| August | 6386. | 25451. | 22-AUG-05:02 |
| September | 3876. | 27184. | 02-SEP-05:02 |
| October | 2108. | 17605. | 07-OCT-11:20 |
| November | 362. | 8524. | 01-NOV-05:08 |
| December | 119. | 7754. | 22-DEC-12:22 |
| Annual Sum or Average | 2685. | ||
| Minimum of Months | 0. | 0. | |
| Maximum of Months | 7601. | 31159. |
Report: COOLING PART LOAD PERFORMANCE
For: VAV_3_COOLC DXCOIL
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| COOLING COIL ELECTRIC POWER [W] | COOLING COIL ELECTRIC POWER {Maximum}[W] | COOLING COIL ELECTRIC POWER {TIMESTAMP} | |
| January | 54. | 6117. | 11-JAN-15:00 |
| February | 0. | 0. | 01-FEB-00:10 |
| March | 160. | 4837. | 22-MAR-15:00 |
| April | 1253. | 10776. | 14-APR-15:00 |
| May | 4140. | 19039. | 02-MAY-14:00 |
| June | 7324. | 30870. | 19-JUN-05:02 |
| July | 8445. | 32171. | 14-JUL-05:02 |
| August | 6764. | 27145. | 22-AUG-05:02 |
| September | 4051. | 29792. | 02-SEP-05:02 |
| October | 2168. | 17866. | 06-OCT-15:32 |
| November | 354. | 8869. | 01-NOV-05:06 |
| December | 117. | 8100. | 22-DEC-13:00 |
| Annual Sum or Average | 2899. | ||
| Minimum of Months | 0. | 0. | |
| Maximum of Months | 8445. | 32171. |
Report: FAN PART LOAD PERFORMANCE
For: VAV_1_FAN
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| FAN ELECTRIC POWER [W] | FAN ELECTRIC POWER {Maximum}[W] | FAN ELECTRIC POWER {TIMESTAMP} | |
| January | 583. | 1432. | 13-JAN-13:10 |
| February | 585. | 1341. | 22-FEB-13:10 |
| March | 570. | 1414. | 11-MAR-13:00 |
| April | 610. | 1366. | 14-APR-13:00 |
| May | 640. | 1656. | 06-MAY-16:30 |
| June | 751. | 2415. | 17-JUN-16:10 |
| July | 844. | 2605. | 08-JUL-16:30 |
| August | 794. | 2354. | 21-AUG-15:10 |
| September | 729. | 2248. | 11-SEP-15:10 |
| October | 667. | 1760. | 04-OCT-15:10 |
| November | 568. | 1445. | 14-NOV-11:50 |
| December | 558. | 1445. | 28-DEC-12:10 |
| Annual Sum or Average | 658. | ||
| Minimum of Months | 558. | 1341. | |
| Maximum of Months | 844. | 2605. |
Report: FAN PART LOAD PERFORMANCE
For: VAV_2_FAN
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| FAN ELECTRIC POWER [W] | FAN ELECTRIC POWER {Maximum}[W] | FAN ELECTRIC POWER {TIMESTAMP} | |
| January | 674. | 1364. | 13-JAN-13:10 |
| February | 645. | 1311. | 22-FEB-13:10 |
| March | 588. | 1405. | 11-MAR-13:10 |
| April | 633. | 1386. | 14-APR-13:00 |
| May | 670. | 2188. | 28-MAY-05:02 |
| June | 735. | 6292. | 18-JUN-05:02 |
| July | 800. | 5185. | 08-JUL-05:02 |
| August | 709. | 3082. | 09-AUG-05:02 |
| September | 699. | 3340. | 02-SEP-05:02 |
| October | 681. | 1753. | 04-OCT-14:30 |
| November | 585. | 1491. | 18-NOV-12:00 |
| December | 600. | 1401. | 28-DEC-12:40 |
| Annual Sum or Average | 668. | ||
| Minimum of Months | 585. | 1311. | |
| Maximum of Months | 800. | 6292. |
Report: FAN PART LOAD PERFORMANCE
For: VAV_3_FAN
Timestamp: 2021-07-16 12:27:08
Custom Monthly Report| FAN ELECTRIC POWER [W] | FAN ELECTRIC POWER {Maximum}[W] | FAN ELECTRIC POWER {TIMESTAMP} | |
| January | 835. | 1305. | 13-JAN-13:10 |
| February | 777. | 1261. | 22-FEB-13:20 |
| March | 638. | 1379. | 11-MAR-13:20 |
| April | 652. | 1473. | 14-APR-15:10 |
| May | 717. | 3148. | 03-MAY-05:02 |
| June | 833. | 7864. | 18-JUN-05:02 |
| July | 914. | 7593. | 14-JUL-05:02 |
| August | 751. | 3340. | 22-AUG-05:02 |
| September | 727. | 3944. | 02-SEP-05:02 |
| October | 692. | 2291. | 07-OCT-05:02 |
| November | 647. | 1481. | 18-NOV-13:05 |
| December | 763. | 1332. | 28-DEC-12:50 |
| Annual Sum or Average | 745. | ||
| Minimum of Months | 638. | 1261. | |
| Maximum of Months | 914. | 7864. |


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I am a full-stack web developer with 20 years of experience implementing interfaces for browsers and architecting back-end systems to support web and mobile applications.
I've also performed with the Juggling Jollies, Skitnik, and the Flying Karamazov Brothers.
Check out my blog!


We are BYU's software engineering club. We prepare students to become great software engineers and connect them with great companies.
These are our events.
This is how to partner with us.
This is about us.


Greetings Citizen! Voting is an important part of our democratic process. If you need to register please click HERE. If you are already registered, find your nearest ballot drobox HERE.
You are the future of our democratic process. In most states citizens are unable to vote until the age of 18, however it is important that you are as informed about the process as possible. Click HERE to find out more info!


CloudBrew started in 2013 as a small-scale event focused on cloud technologies and Microsoft Azure.
Two full days of Microsoft Azure, with several tracks to choose from! We will have some local and international speakers who will share their knowledge and experience.
The event takes place in a former brewery. A lunch buffet will take care of our inner selves. After all, brains that learn need energy!
We try to balance the size of CloudBrew to give the event a more intimate feeling, where everyone is approachable - attendees and speakers. Two days of learning that are really worth everyone's valuable time.
Organized by AZUG, the Belgium Microsoft Azure User Group, CloudBrew originates from the community. Both you and our speakers are part of it. Ideal to network with peers!
Click on any session to see more details.
Tip: install the CloudBrew App and always have the agenda at hand.
Click on any speaker to see more details.
CloudBrew takes place at Lamot, Van Beethovenstraat 8-10, 2800 Mechelen (Belgium)
The heart of Mechelen is a car-free zone. We encourage our visitors to use public transport. Mechelen has a direct connection by train from most other cities in Belgium.
If you choose to come by car, you can use the relatively inexpensive peripheral car parks "Zandpoortvest" or "Douaneplein". These car parks are all within walking distance (1.2 – 1.6km) of Lamot. Q-Park Lamot and Indigo parkings are closer by the venue.
Check Lamot's website for up-to-date parking and public transportation options. More parkings in Mechelen can be found on the Toerisme Mechelen website.


{% extends "template.html" %} {% block page_content %}
1. Monitoring the CPU usage, IO wait, Memory, Disk IO, Network, and TCP connections of the server.
2. Monitoring the CPU usage, Context switching, Memory, Disk read and write, and TCP connections of the specified port.
3. For Java applications, monitoring size of JVM and Garbage collection; when the frequency of Full GC is too high, an email alert will be sent.
4. When the Server CPU usage is too high, or free memory is too low, an email alert will be sent; And can clear the cache automatically.
5. Start or Stop monitoring specified port at any time.
6. When the port restarts, it can automatically re-monitor.
7. Support Operational monitoring, when the port stopped, an email alert will be sent.
8. Stopping the Agent directly on the Server.
9. Monitoring data can be visualized according to the specified time period.
10. Calculate the percentiles of the CPU, Disk IO, and Network.
11. Monitoring data sampling frequency is up to about 1 time/sec, and any frequency can be set.
12. The real-time usage of server(CPU, Memory, and Disk) can be viewed directly on the page.
13. A Server can manager multiple Agents at the same time.
14. If Server is stopped, it doesn't affect the monitoring of Agent.
1. Framework: aiohttp
.
2. Template rendering: jinja2
.
3. Visualization: echarts
.
4. Thread pool and queue to monitor.
5. Database: InfluxDB
.
6. In order to ensure the accuracy of the monitoring data, use Linux's commands to get data directly, and there is no curve fitting during visualization.
Clone performance_monitor
git clone https://github.com/leeyoshinari/performance_monitor.git
The server folder is the Server, and only one needs to be deployed; the agent folder is Agent, and deployed on the servers that need to be monitored.
Deploy InfluxDB, installation steps on CentOS are as follows:
(1) Download and install
wget https://dl.influxdata.com/influxdb/releases/influxdb-1.8.3.x86_64.rpm
yum localinstall influxdb-1.8.3.x86_64.rpm
(2) Start
systemctl enable influxdb
systemctl start influxdb
(3) Modify configuration
vim /etc/influxdb/influxdb.conf
Around line 256, modify port: bind-address = ":8086"
Around line 265, log disable: log-enabled = false
Restart InfluxDB
(4) Create database
create database test
use test
create user root with password '123456'
grant all privileges on test to root
Respectively modify the configuration files config.ini
in the server and agent folders.
Check the version of sysstat
. Respectively use commands iostat -V
and pidstat -V
, the version of 12.4.0
has been tested, if not, please click me to download it.
Respectively run server.py
in server and agent folders.
nohup python3 server.py &
Visit pages
(1) After Agent is started, visit http://ip:port
, you can see the server's data(CPU, Meeory, Disk, Network, etc.).
(2) After server is started, visit http://ip:port/context
, you can see some informations about Agents.
Click the IP in the list, some information of the Agent will be shown.
(3) Click MonitorList tab to see the monitoring page.
Select server and enter the port, then click the startMonitor button to start monitoring the port on the selected server; click the stopMonitor button to stop monitoring the port on the selected server.
(4) Click Visualize tab to see the visualization page. Select server, type, disk, startTime and endTime, then click the Visualize button to visualize the monitoring data.
Using pyinstaller
to package python code. After packaging, it can be quickly deployed agent on other servers without installing python3.7+ and third-party packages.
Before packaging, you must ensure that the python code can run normally.
Package server
(1) Enter the server folder, run:
pyinstaller -F server.py -p draw_performance.py -p config.py -p Email.py -p logger.py -p process.py -p request.py -p __init__.py --hidden-import draw_performance --hidden-import config --hidden-import logger --hidden-import Email --hidden-import process --hidden-import request
(2) Enter dist
folde, find the executable file server
,
(3) Copy config.ini
to the dist
folder,
(4) Copy templates
and static
to the dist
folder,
(5) Start server
nohup ./server &
Package agent
(1) Enter agent folder, run:
pyinstaller -F server.py -p performance_monitor.py -p logger.py -p common.py -p config.py -p __init__.py --hidden-import logger --hidden-import performance_monitor --hidden-import common --hidden-import config
(2) Enter dist
folde, find the executable file server
,
(3) Copy config.ini
to the dist
folder,
(4) Copy the dist
folder to other servers, and start server
nohup ./server &
NOTE: Since the agent
needs to run on the server to be monitored, the executable file packaged on the server of the CentOS system X86 architecture can only run on the server of the CentOS system X86 architecture; servers of other system and architecture need to be repackaged agent
.
ps
, jstat
, pidstat
, iostat
and netstat
, if not, please install it.sysstat
must be 12+, the 12 version has been tested, other versions have not been tested, and using old version may cause data abnormalities; please click me to download the latest version.CentOS
, Ubuntu
, KylinOS
, NeoKylin
, support X86_64
and ARM
architecture.draw_performance1.py
to draw_performance.py
, and rename performance_monitor1.py
to performance_monitor.py
. If you know how to solve the issue, please tell me, thank you very much!


State: 1, form-id: form1 (xpath: None)
Input values:
Type: castman, id: username (xpath: //*[@id="username"]), value: None
Type: p@ssw0rd, id: password (xpath: ), value: None
Path to the form:
id: exe1 (xpath: //html/body/button[1])
id: exe2 (xpath: //html/body/button[1])
Clickables in the form:
id: btn1 (xpath: //html/body/button[1])
id: btn2 (xpath: //html/body/button[1])


open fun getCurrentZoom(): Float
Get the current zoom. This is the zoom relative to the initial scale, not the original resource.
Return current zoom multiplier.


Uses of Class
com.jme.light.Light
-
Packages that use Light Package Description com.jme.light com.jme.scene com.jme.scene.control -
-
Uses of Light in com.jme.light
Subclasses of Light in com.jme.light Modifier and Type Class Description class
AmbientLight
An ambient light adds a constant color to the scene.class
DirectionalLight
DirectionalLight
is a light coming from a certain direction in world space.class
LightProbe
A LightProbe is not exactly a light.class
PointLight
Represents a point light.class
SpotLight
Represents a spot light.Methods in com.jme.light that return Light Modifier and Type Method Description Light
Light. clone()
Light
LightList. get(int num)
Methods in com.jme.light that return types with arguments of type Light Modifier and Type Method Description java.util.Iterator<Light>
LightList. iterator()
Returns an iterator that can be used to iterate over this LightList.Methods in com.jme.light with parameters of type Light Modifier and Type Method Description void
LightList. add(Light l)
Adds a light to the list.void
LightList. remove(Light l)
Removes the given light from the LightList. -
Uses of Light in com.jme.scene
Methods in com.jme.scene that return Light Modifier and Type Method Description Light
LightNode. getLight()
Methods in com.jme.scene with parameters of type Light Modifier and Type Method Description void
Spatial. addLight(Light light)
addLight
adds the given light to the Spatial; causing all child Spatials to be affected by it.void
Spatial. removeLight(Light light)
removeLight
removes the given light from the Spatial.void
LightNode. setLight(Light light)
Constructors in com.jme.scene with parameters of type Light Constructor Description LightNode(java.lang.String name, Light light)
-
Uses of Light in com.jme.scene.control
Methods in com.jme.scene.control that return Light Modifier and Type Method Description Light
LightControl. getLight()
Methods in com.jme.scene.control with parameters of type Light Modifier and Type Method Description void
LightControl. setLight(Light light)
Constructors in com.jme.scene.control with parameters of type Light Constructor Description LightControl(Light light)
LightControl(Light light, LightControl.ControlDirection controlDir)
-


About our Data
Santa Clara
The units you see here are census blocks. Data for this module were obtained from the US Census Bureau. The block shapefile for the City of Santa Clara was downloaded from the Census's TIGER/Line Shapefiles. Demographic information from the 2010 Decennial Census was downloaded at the block level from American FactFinder. The source shapefile with demographic information is available for download from MGGG's GitHub organization @gerrymandr.
This module was created because of a lawsuit against Santa Clara's at-large electoral system for City Council, which plaintiffs claim posed a structural impediment to the election of Asian Americans. A district court in May 2018 ruled that the voting system violated the California Voting Rights Act (for more information see here. In February 2018, MGGG released a report with recommendations for alternative voting systems in Santa Clara.
Alaska
The units used here are voting precincts. Data for this module were obtained from the Alaska Division of Elections and from National Historic GIS (NHGIS). The precinct shapefile and election results were downloaded from the Alaska Division of Elections. Demographic information from the 2010 Decennial Census was downloaded at the block level from NHGIS. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
This module was created to aid in a paper that MGGG is currently working on about Alaska's state senate districts.
Texas
The units used here are voting tabulation districts or VTDS. Data for this module were obtained from the Texas Legislative Council. The VTD shapefile and election results were downloaded from the Texas Legislative Council's Texas Redistricting website. Demographic data were compiled by the Texas Legislative Council using 2010 Decennial Census data. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
Austin
The units you see here are census blocks. Data for this module were obtained from the US Census Bureau. The block shapefile for the City of Austin was downloaded from the Census TIGER/Line Shapefiles. Demographic information from the 2010 Decennial Census was downloaded at the block level from American FactFinder. The source shapefile with demographic information is available for download from MGGG's GitHub organization @gerrymandr.
Wisconsin
The units you see here are wards, which are Wisconsin's precinct equivalents. All election data for this module were obtained from the Wisconsin State Legislature. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
In June 2018, the US Supreme Court remanded back to the lower courts Gill v. Whitford, a case challenging Wisconsin's state assembly districts as an unconstitutional partisan gerrymander.
Missouri
The units used here are voting tabulation districts or VTDs. Data for this module were obtained from the Harvard Election Data Archive. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
This shapefile and corresponding election data are from the 2008 election cycle. In November 2018, Missouri passed a constitutional amendment that makes the state demographer responsible for drawing the state's legislative districts, rather than the state legislature itself. MGGG is currently working to compile a more recent precinct shapefile with election results, but Missouri is a notoriously difficult state to do this because of discrepancies between election results reported by counties and the census provided VTDs.
Chicago
Community Areas
The units used here are community areas, which were drawn by social scientists at UChicago in the 1920s and serve as proxies for neighborhoods. Data for this module were obtained the City of Chicago Data Portal and from the US Census Bureau. The community area shapefile was downloaded from the Chicago Data Portal. Demographic information from the 2010 Decennial Census was downloaded at the block level from American FactFinder. The source shapefile with demographic information is available for download from MGGG's GitHub organization.
In April 2019, MGGG published a study on various reform proposals for the Chicago City Council. Our findings strongly support a transition to multimember wards with ranked choice voting to secure and sustain fair representation.
Precincts
The units used here are precincts. Data for this module were obtained the City of Chicago Data Portal and from the US Census Bureau. The precinct shapefile was downloaded from the Chicago Data Portal. Demographic information from the 2010 Decennial Census was downloaded at the block level from American FactFinder. The source shapefile with demographic information is available for download from MGGG's GitHub organization.
In April 2019, MGGG published a study on various reform proposals for the Chicago City Council. Our findings strongly support a transition to multimember wards with ranked choice voting to secure and sustain fair representation.
Mississippi
The units used here are census block groups. Data for this module were obtained from the US Census Bureau. The block group shapefile for Mississippi was downloaded from the Census's TIGER/Line Shapefiles. Demographic information from the 2010 Decennial Census was downloaded at the block level from American FactFinder. The source shapefile with demographic information is available for download from MGGG's GitHub organization @gerrymandr.
This Mississippi module comes out of a project from MGGG's Voting Rights Data Institute that explored how many majority-Black congressional districts are possible to draw. A write-up of this experiment is forthcoming.
Georgia
The units you see here are precincts. Data for this module were obtained from the Georgia General Assembly Legislative and Congressional Reapportionment Office, the MIT Elections Data and Science Lab (MEDSL), and National Historic GIS (NHGIS). The precinct shapefile for Georgia was downloaded from the Georgia General Assembly Legislative and Congressional Reapportionment Office's website and 2016 election results were downloaded from the MEDSL. Demographic information from the 2010 Decennial Census was downloaded at the block level from NHGIS. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
North Carolina
The units used here are voting tabulation districts or VTDS. Data for this module were obtained from the North Carolina General Assembly and National Historic GIS (NHGIS). The VTD shapefile and election results were downloaded from the North Carolina General Assembly's 2016 Redistricting Reference Data. Demographic information from the 2010 Decennial Census was downloaded at the block level from NHGIS. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
In March 2019 the US Supreme Court heard oral arguments for Rucho v. Common Cause, in which plaintiffs claim that North Carolina's remedial 2016 congressional map is an unconstitutional partisan gerrymander. The 2016 remedial map had been drawn after the Supreme Court struck down two of the state's 2011 enacted congressional districts as unconstitutional racial gerrymanders. MGGG and our collaborators filed an amicus brief which can be accessed on our website.
Pennsylvania
The units used here are voting tabulation districts, or VTDs. The raw, unprocessed VTDs were collected from the Census TIGER/Line Shapefiles. Election data was compiled at the precinct level by a private individual and was found to be within 1% of the official state-reported results at the county and state levels. Demographic information from the 2010 Decennial Census was downloaded at the block level from American FactFinder. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
In February 2018, Moon Duchin was asked by Governor Tom Wolf to provide guidance in evaluating redistricting plans for fairness after the Pennsylvania Supreme Court struck down the 2011 enacted plan as an unconstitutional partisan gerrymander. Her report is hosted on MGGG's website.
Islip
The units you see here are census blocks. Data for this module were obtained from the US Census Bureau. The block shapefile for the City of Islip was downloaded from the Census TIGER/Line Shapefiles. Demographic information from the 2010 Decennial Census was downloaded at the block level from American FactFinder. The source shapefile with demographic information is available for download from MGGG's GitHub organization @gerrymandr.
Massachusetts
Towns
The units you see here are Massachusetts' towns and municipalities. Data for this module were obtained from the Massachusetts Elections Division website and the Massachusetts Bureau of Geographic Information (MassGIS). The town shapefile was downloaded from the MassGIS website. All election data comes from the Secretary of the Commonwealth's Elections Division website.
This module was created to aid in a paper, "Locating the representational baseline: Republicans in Massachusetts," written by MGGG and collaborators. Despite Republicans usually gaining between 30% and 40% of the two-way vote share in statewide elections, Massachusetts has not elected a Republican to the US House of Representatives since 1994. The paper finds that the underperformance of Republicans in Massachusetts is not the result of gerrymandering or candidate availability, but rather is a factor of the spatial distribution of Republican voters in the state.
Precincts, 2002-2010
The units you see here are precincts from the 2000 redistricting cycle. The 2002-2010 raw, unprocessed shapefile comes from the United States Census Bureau and was sent to MGGG by Secretary of the Commonwealth Francis Galvin's office. All election data comes from the Secretary of the Commonwealth's Elections Division website. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
This module was created to aid in a paper, "Locating the representational baseline: Republicans in Massachusetts," written by MGGG and collaborators. Despite Republicans usually gaining between 30% and 40% of the two-way vote share in statewide elections, Massachusetts has not elected a Republican to the US House of Representatives since 1994. The paper finds that the underperformance of Republicans in Massachusetts is not the result of gerrymandering or candidate availability, but rather is a factor of the spatial distribution of Republican voters in the state.
Precincts, 2012-2016
The units you see here are precincts from the 2010 redistricting cycle. The 2012-2016 raw, unprocessed precinct shapefile comes from the office of the Secretary of the Commonwealth of Massachusetts, William Francis Galvin. All election data comes from the Secretary of the Commonwealth's Elections Division website. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
This module was created to aid in a paper, "Locating the representational baseline: Republicans in Massachusetts," written by MGGG and collaborators. Despite Republicans usually gaining between 30% and 40% of the two-way vote share in statewide elections, Massachusetts has not elected a Republican to the US House of Representatives since 1994. The paper finds that the underperformance of Republicans in Massachusetts is not the result of gerrymandering or candidate availability, but rather is a factor of the spatial distribution of Republican voters in the state.
Lowell
The units you see here are census blocks. Data for this module were obtained from the US Census Bureau. The block shapefile for the City of Lowell was downloaded from the Census TIGER/Line Shapefiles. Demographic information from the 2010 Decennial Census was downloaded at the block level from American FactFinder. The source shapefile with demographic information is available for download from MGGG's GitHub organization @gerrymandr.
This module was created in response to the current lawsuit (Huot et al. v. City of Lowell et al.) challenging the City of Lowell's at-large electoral system for City Council and School Committee. Plaintiffs in the case argue that the current system dilutes the votes of Lowell's communities of color. For more information about the lawsuit, read the LCCR's explainer here.
Vermont
The units you see here are towns. Data for this module were obtained from the Vermont Open Geodata Portal, the Vermont Secretary of State, and the US Census Bureau. The town shapefile was downloaded from the Vermont Open Geodata Portal and election results were downloaded from the Secretary of State's website. Vermont does not release election results for towns with a total population under 50, thus 9 towns have been excluded from this module. Demographic information from the 2010 Decennial Census was downloaded at the sub-county level from American FactFinder. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
Utah
The units you see here are voting precincts. Data for this module were obtained from the Utah Automated Geographic Reference Center (AGRC), the MIT Elections Data and Science Lab (MEDSL), and National Historic GIS (NHGIS). The precinct shapefile was downloaded from the Utah AGRC and 2016 election results were downloaded from the MEDSL. Demographic information from the 2010 Decennial Census was downloaded at the block level from NHGIS. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
In November 2018, Utah narrowly passed Proposition 4, a ballot initiative which shifted the responsibility of redistricting for Utah's state legislative and congressional districts from the state legislature to an independent commission.
Virginia
The units you see here are voting precincts. Data for this module were obtained from the Princeton Gerrymandering Project and National Historic GIS (NHGIS). The precinct shapefile was compiled and joined to election results by the Princeton Gerrymandering Project. Demographic information from the 2010 Decennial Census was downloaded at the block level from NHGIS. The source shapefile for this module, along with additional information about processing, is available for download from MGGG's GitHub organization @mggg-states.
In June 2018, the Eastern District Court of Virginia ruled 11 of the House of Delegates districts unconstitutional racial gerrymanders. MGGG published a report evaluating the 2011 Enacted House of Delegates plan as well as several of the proposed alternatives.


Key Word(s): class repo, conda, FAS onDemand, Setup, statsmodels
CS109B Data Science 2: Advanced Topics in Data Science
Lecture 3 - Coding Environment
Setup and review of statsmodels
¶
Notebook B¶
Harvard University
Spring 2021
Instructors: Mark Glickman, Pavlos Protopapas, and Chris Tanner
Additional Instructor: Eleni Kaxiras
Content: Eleni Kaxiras and Will Claybaugh
## RUN THIS CELL TO PROPERLY HIGHLIGHT THE EXERCISES
import requests
from IPython.core.display import HTML
styles = requests.get("https://raw.githubusercontent.com/Harvard-IACS/2019-CS109B/master/content/styles/cs109.css").text
HTML(styles)
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
Learning Goals¶
By the end of this lab, you should be able to:
- use
np.linalg.vander
- use the weird R-style formulas in
statsmodels
- practice least-squares regression in
statsmodels
Basis Functions¶
In our models we can use various types of functions as basis functions. Strictly speaking, in linear
algebra where a basis for a subspace S of $\mathbb{R}^n$ is a set of vectors that spans S and is
linearly independent. As a reminder, a set of vectors $\textbf{v}_1, \textbf{v}_2, ...,
\textbf{v}_k$ are considered linearly independent if they cannot be written as a linear combination
of each other, such that, if: $c_1\textbf{v}_1+c_2\textbf{v}_2+ ...+ c_k\textbf{v}_k = \textbf{0}$
then $c_1,c_2,...,c_k$ are all zero.
In data science where we have lots of imperfect data (with errors), as well as imperfect computers
(with round-off errors), when we substitute their values into the matrices we almost always get
column degeneracy, meaning, some of our columns become linear combinations of each other. Especially
so if we use the monomial basis and go beyond ~5,6 degree of the polynomial.
Examples are:
- Monomials such as $x,x^2,x^4,x^5$
- Sigmoid/ReLU functions (neural networks)
- Fourier functions
- Wavelets
- Splines
The matrix produced when we substitute the values of our data into the basis functions is called the design matrix.
Linear/Polynomial Regression¶
We will use the diabetes
dataset.
Variables are:
- subject: subject ID number
- age: age diagnosed with diabetes
- acidity: a measure of acidity called base deficit Response:
- y: natural log of serum C-peptide concentration
Original source is Sockett et al. (1987) mentioned in Hastie and Tibshirani's book "Generalized Additive Models".
Reading the data in Pandas:
diab = pd.read_csv("data/diabetes.csv")
diab.head()
Create the design matrix for a fictitious dataset¶
Let's keep just the age
feature and create some columns of our own. Let's see how good
this matrix is before we create the design matrix.
diab_age = diab[['age']].copy()
diab_age['age2'] = diab_age.apply(lambda row: row['age']**2, axis=1)
diab_age['random'] = np.random.normal(0,1,len(diab_age))
diab_age['same'] = diab_age['age']
diab_age.head()
A = diab_age.to_numpy(copy=True)
A[:5]
Let's check if the columns of A are linearly independent by using some linear algebra methods from
numpy.linalg
and sympy
.
from numpy.linalg import matrix_rank
matrix_rank(A)
# check out which rows are linearly independent
import sympy
_, inds = sympy.Matrix(A).T.rref()
inds
np.linalg.cond(A)
Create the design matrix for age
using a polynomial basis¶
Let's keep just the age
feature again and create the design matrix using a polynomial of
degree n
. First we will use the basic numpy
formula vander()
.
vand = np.vander(diab_age.age, 2, increasing=True)
vand[:3], vand.shape
## To our point why the Vandermonde matrix is usually ill-conditioned,
## find the condition number of this matrix
np.linalg.cond(vand), matrix_rank(vand)
Change the degree of the polynomial and comment on what happens to the condition and rank of the matrix.
vand = np.vander(diab_age.age, 8, increasing=True)
vand[:3], vand.shape
## To our point why the Vandermonde matrix is usually ill-conditioned,
## find the condition number of this matrix
np.linalg.cond(vand), matrix_rank(vand)
Linear/Polynomial regression with statsmodels.¶
As you remember from 109a, we have two tools for Linear Regression:
statsmodels
https://www.statsmodels.org/stable/regression.html, andsklearn
https://scikit-learn.org/stable/index.html
Previously, in this notebook, we worked from a vector of target values and a design matrix we built
ourself. In 109a we used e.g. sklearn
's PolynomialFeatures to build the matrix. Now we
will look at statsmodels
which allows users to fit statistical models using R-style
formulas. They build the target value and design matrix for you.
Note: Categorical features (e.g. let's say we had a categorical feature called
Region, are designated by C(Region)
), polynomial features (e.g. age) are entered as
np.power(age, n)
where n
is the degree of the polynomial
OR np.vander(age, n, increasing=True)
.
# Example: if our target variable is 'Lottery', while 'Region' is a categorical predictor and all the others are numerical:
df = dta.data[['Lottery', 'Literacy', 'Wealth', 'Region']]
formula='Lottery ~ Literacy + Wealth + C(Region) + Literacy * Wealth'
For more on these formulas see:
import statsmodels.formula.api as smf
model1 = smf.ols('y ~ age', data=diab)
fit1_lm = model1.fit()
Let's build a dataframe to predict values on (sometimes this is just the test or validation set). Very useful for making pretty plots of the model predictions - predict for TONS of values, not just whatever's in the training set.
x_pred = np.linspace(0.5,20,100)
predict_df = pd.DataFrame(data={"age":x_pred})
predict_df.head()
Use get_prediction(
).summary_frame()
to get the model's prediction (and error bars!)
prediction_output = fit1_lm.get_prediction(predict_df).summary_frame()
prediction_output.head()
Plot the data, the fitted model, the confidence intervals, and the prediction
intervals. For more on how statsmodels
calculates these intervals see: https://www.statsmodels.org/stable/_modules/statsmodels/regression/_prediction.html
ax1 = diab.plot.scatter(x='age',y='y',c='brown',title="Diabetes data with least-squares linear fit")
ax1.set_xlabel("Age at Diagnosis")
ax1.set_ylabel("Log C-Peptide Concentration")
ax1.plot(predict_df.age, prediction_output['mean'],color="green")
ax1.plot(predict_df.age, prediction_output['mean_ci_lower'], color="blue",linestyle="dashed")
ax1.plot(predict_df.age, prediction_output['mean_ci_upper'], color="blue",linestyle="dashed")
ax1.plot(predict_df.age, prediction_output['obs_ci_lower'], color="green",linestyle="dashdot")
ax1.plot(predict_df.age, prediction_output['obs_ci_upper'], color="green",linestyle="dashdot");
- Fit a 3rd degree polynomial model to predict
y
using onlyage
and - Plot the model and its confidence intervals.
- Change the degree of your polynomial and see what happens to the fitted curve.
- Does our model have an intercept? Note: we can discover the existence or not of an
intercept in our model by running:
model_name.params
# your answer here
# solution
It's easier to build higher order polynomials using np.vandrer()
.
formula = "y ~ np.vander(age, 6, increasing=True) -1"
fit3_lm = smf.ols(formula=formula, data=diab).fit()
fit3_lm.params
## To our point why the Vandermonde matrix is usually ill-conditioned,
# find the condition number of this matrix
np.linalg.cond(np.vander(predict_df.age, 6, increasing=True))
# solution
poly_predictions = fit3_lm.get_prediction(predict_df).summary_frame()
poly_predictions.head()
# solution
x_pred = np.linspace(0.5,15,100)
predict_df = pd.DataFrame(data={"age":x_pred})
ax2 = diab.plot.scatter(x='age',y='y',c='Red',title="Diabetes data with least-squares cubic fit")
ax2.set_xlabel("Age at Diagnosis")
ax2.set_ylabel("Log C-Peptide Concentration")
ax2.plot(predict_df.age, poly_predictions['mean'],color="green")
ax2.plot(predict_df.age, poly_predictions['mean_ci_lower'], color="blue",linestyle="dashed", label='confidence interval')
ax2.plot(predict_df.age, poly_predictions['mean_ci_upper'], color="blue",linestyle="dashed");
ax2.legend();
QR decomposition (Beyond the scope of this class)¶
As you know, to find the parameters of our model, we may try to solve the so-called normal
equations, which, written in matrix form, are:
\begin{equation}
(\boldsymbol{A^T}\cdot \boldsymbol{A}) \cdot \boldsymbol{b} = \boldsymbol{A} \cdot \boldsymbol{y}
\end{equation}
The direct solution is $\hat{\boldsymbol{b}}=(\boldsymbol{A}^T\cdot \boldsymbol{A})^{-1}\cdot \boldsymbol{A}^T \cdot \boldsymbol{y}$
Solving the least-squares problem directly via the normal equations is susceptible to roundoff error
when the condition of the matrix $\boldsymbol{A}$ is large. An alternative technique involves QR
decomposition (details in any good linear algebra book). statsmodels
lets you use this
technique via a parameter in the .fit
:
.fit(method='qr')
Let's try with QR now¶
formula = "y ~ np.vander(age, 6, increasing=True) -1"
fit3_lm = smf.ols(formula=formula, data=diab).fit(method='qr')
fit3_lm.params
# solution
poly_predictions = fit3_lm.get_prediction(predict_df).summary_frame()
poly_predictions.head()
# solution
ax2 = diab.plot.scatter(x='age',y='y',c='Red',title="Diabetes data with least-squares cubic fit")
ax2.set_xlabel("Age at Diagnosis")
ax2.set_ylabel("Log C-Peptide Concentration")
ax2.plot(predict_df.age, poly_predictions['mean'],color="green")
ax2.plot(predict_df.age, poly_predictions['mean_ci_lower'], color="blue",linestyle="dashed", label='confidence interval')
ax2.plot(predict_df.age, poly_predictions['mean_ci_upper'], color="blue",linestyle="dashed");
ax2.legend();


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