1. USC Center for Energy Informatics cei.usc.edu Towards Modeling and Prediction of Energy Consumption for a Campus Micro-Grid Saima Aman

2. USC Center for Energy Informatics cei.usc.edu Energy Consumption  USC has 250 buildings (2009), up by 10% from from 2001  Annual consumption of electricity in 2009 was up by 37% from 2001  Majority of electricity is consumed in buildings  Modeling useful for planning and implementing university energy policies 9-Oct-152

3. USC Center for Energy Informatics cei.usc.edu Overview Goal: Models to predict daily energy use Trained using energy data for two years (2008 & 2009)  Campus-Scale Energy Use Model  Covers 170 buildings on UPC and HSC campuses  Building-Scale Energy Use Model  Covers 23 buildings on the UPC campus

4. USC Center for Energy Informatics cei.usc.edu Related Work  Modeling methods:  Regression models, artificial neural networks, time series models  Data used in Models  Static consumption data  Live data streams  Synthetic data using building simulation programs (e.g., Energy Plus)  Estimation based on utility bills.

5. USC Center for Energy Informatics cei.usc.edu Related Work : Data Attributes  Weather Data  Temperature measurements (max, avg)  Heating degree day, cooling degree day  Building Data  Orientation of buildings; windows  Wall insulation thickness, heat transfer coefficient; window to wall ratio, etc.  Occupancy data  Estimate presence/absence and number of people  Based on sensors in rooms, building entrances  Based on heuristics, such as open/close office door

6. USC Center for Energy Informatics cei.usc.edu Unique Features of our Work  Single unified building energy use model  Applicable to diverse buildings; other works focus on homogeneous buildings  Information driven approach  Indirect indicators of energy use plus domain attributes; data is typically available publicly  Design and Operation phase  Use attributes that can be applied during design phase as well

7. USC Center for Energy Informatics cei.usc.edu FMS Energy Data  15-min interval energy data available for 3 years (from Jul 09, 2007 to Nov 21, 2010)  Covers 170 buildings on the UPC and HSC campus  Data: One CSV file for each day  24*4 = 96 records for each building per day  Issues: Missing values and timestamps 9-Oct-157

8. USC Center for Energy Informatics cei.usc.edu Model Attributes  ENRG* - Energy Use  TMP* - Max Temperature Value  AVTMP - Average Temperature Value  GAREA - Gross Area  NAREA - Net Area  CYR - Year of construction (1919 – 2006)  BTYP - Type of building (Academic, Residential, Other)  WKDY* - Day of the week (M,T,…Su)  HLDY* - Holiday (None, Academic Holiday, Campus Holiday)  SEM* - Semester (Spring, Summer, Fall) (Sources: FMS, Academic calendar, Weather Underground) ( Attributes marked * are used in campus-scale model)

9. USC Center for Energy Informatics cei.usc.edu Daily consumption for 2009 & 2008 9-Oct-159 SpringSummer Fall

10. USC Center for Energy Informatics cei.usc.edu Academic Buildings (2009) 9-Oct-1510  EEB – Hughes (5F+B, 61252 sq. ft., 1990 )  RTH – Tutor Hall (6F+B, 102797 sq. ft., 2003)

11. USC Center for Energy Informatics cei.usc.edu Residential Buildings (2009)  WTO – Webb Twr (14F+B, 107481 sq.ft., 1972)  PRB – Parkside (4F+B, 131657 sq. ft., 2006) 9-Oct-1511

12. USC Center for Energy Informatics cei.usc.edu Campus-scale Model  Training Data  731 records  (for each day of the year 2008 & 2009)  Test Data  325 records  (for the year 2010, up to Nov 21)  Tool: Statistics toolbox of MATLAB

13. USC Center for Energy Informatics cei.usc.edu Decision Tree 1. if TMP =74.5 then node 3 else 462970 2. if WKDY /in/ {6/7} then node 4 else if WKDY/in/ {1/2/3/4/5} then node 5 else 430815 3. if WKDY /in/ {6/7} then node 6 else if WKDY/in/ {1/2/3/4/5} then node 7 else 488709 4. if HLDY /in/ {1/2} then node 8 else if HLDY=0 then node 9 else 393055 5. if HLDY/in/ {1/2} then node 10 else if HLDY=0 then node 11 else 446880

14. USC Center for Energy Informatics cei.usc.edu 9-Oct-1514

15. USC Center for Energy Informatics cei.usc.edu Campus-scale evaluation  Model used to make prediction for year 2010  Evaluated using observed values  CV-RMSE value = 7.45%.  The predicted values are able to capture the weekly patterns of rise and fall of energy load.

16. USC Center for Energy Informatics cei.usc.edu Campus-scale Energy Prediction 9-Oct-1516 (For the year 2010)

17. USC Center for Energy Informatics cei.usc.edu Building-scale Model  Training Data  17544 records  Test Data  Separate test dataset for each building  Each has 325 records

18. USC Center for Energy Informatics cei.usc.edu Decision Tree 1. if NAREA<41935.5 then node 2 elseif NAREA>=41935.5 then node 3 else 3316.57 2. if CYR =1990.5 then node 5 else 940.692 3. if CYR =1931 then node 7 else 4742.1 4. if CYR =1960.5 then node 9 else 756.139 5. if WKDY in {6 7} then node 10 elseif WKDY in {1 2 3 4 5} then node 11 else 2417.12 6. if WKDY in {6 7} then node 12 elseif WKDY in {1 2 3 4 5} then node 13 else 2160.91 7. if GAREA<100310 then node 14 elseif GAREA>=100310 then node 15 else 5139.21 8. if GAREA<40162.5 then node 16 elseif GAREA>=40162.5 then node 17 else 246.935 9. if GAREA<358890 then node 18 elseif GAREA>=358890 then node 19 else 925.873 10. if WKDY=7 then node 20 elseif WKDY=6 then node 21 else 1917.94

19. USC Center for Energy Informatics cei.usc.edu Building-scale evaluation

20. USC Center for Energy Informatics cei.usc.edu Building-scale Prediction  ASC (CV-RMSE = 12.03%)

21. USC Center for Energy Informatics cei.usc.edu Building-scale Prediction  EEB (CV-RMSE = 9.19%)

22. USC Center for Energy Informatics cei.usc.edu Future Work  Include fine-grained information in our model  15-min granularity energy use data  Detailed occupancy data (classroom assignments, course enrolment, room use)

23. USC Center for Energy Informatics cei.usc.edu Thanks

24. USC Center for Energy Informatics cei.usc.edu Academic Buildings (2009)  SAL – Salvatori CS (3F, 37521 sq. ft., 1976)  Comparison 9-Oct-1524

25. USC Center for Energy Informatics cei.usc.edu Residential Buildings (2009)  PTD – Pardee Tower (8F, 59209 sq. ft., 1982)  Comparison 9-Oct-1525