usc center for energy informatics cei.usc.edu towards modeling and prediction of energy consumption...
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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
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USC Center for Energy Informatics cei.usc.edu
Overview
Goal: Models to predict daily energy useTrained 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
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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)
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Daily consumption for 2009 & 2008
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Spring Summer Fall
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Academic Buildings (2009)
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EEB – Hughes (5F+B, 61252 sq. ft., 1990 )
RTH – Tutor Hall (6F+B, 102797 sq. ft., 2003)
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)
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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 2 else 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
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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.
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Campus-scale Energy Prediction
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(For the year 2010)
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Building-scale Model
Training Data 17544 records
Test Data Separate test dataset for each building Each has 325 records
USC Center for Energy Informatics cei.usc.edu
Decision Tree1. if NAREA<41935.5 then node 2 elseif
NAREA>=41935.5 then node 3 else 3316.57 2. if CYR<1990.5 then node 4 elseif CYR>=1990.5
then node 5 else 940.692 3. if CYR<1931 then node 6 elseif CYR>=1931
then node 7 else 4742.1 4. if CYR<1960.5 then node 8 elseif 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
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Building-scale evaluation
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Building-scale Prediction
ASC (CV-RMSE = 12.03%)
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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)
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Thanks
USC Center for Energy Informatics cei.usc.edu
Academic Buildings (2009)
SAL – Salvatori CS (3F, 37521 sq. ft., 1976)
Comparison
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Residential Buildings (2009)
PTD – Pardee Tower (8F, 59209 sq. ft., 1982)
Comparison
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