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

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

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

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.

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

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

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

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)

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

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

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

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

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

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

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.

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

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

USC Center for Energy Informatics cei.usc.edu Decision Tree 1. if NAREA< then node 2 elseif NAREA>= then node 3 else if CYR = then node 5 else if CYR =1931 then node 7 else if CYR = then node 9 else if WKDY in {6 7} then node 10 elseif WKDY in { } then node 11 else if WKDY in {6 7} then node 12 elseif WKDY in { } then node 13 else if GAREA< then node 14 elseif GAREA>= then node 15 else if GAREA< then node 16 elseif GAREA>= then node 17 else if GAREA< then node 18 elseif GAREA>= then node 19 else if WKDY=7 then node 20 elseif WKDY=6 then node 21 else

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

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

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

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)

USC Center for Energy Informatics cei.usc.edu Thanks

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

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