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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

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Page 1: 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

Towards Modeling and Prediction of Energy Consumption for a Campus Micro-Grid

Saima Aman

Page 2: 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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Page 3: 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

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

Page 4: 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

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.

Page 5: 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

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

Page 6: 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

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

Page 7: 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

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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Page 8: 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

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)

Page 9: 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

Daily consumption for 2009 & 2008

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Spring Summer Fall

Page 10: 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

Academic Buildings (2009)

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EEB – Hughes (5F+B, 61252 sq. ft., 1990 )

RTH – Tutor Hall (6F+B, 102797 sq. ft., 2003)

Page 11: 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

Residential Buildings (2009) WTO – Webb Twr (14F+B, 107481 sq.ft., 1972)

PRB – Parkside (4F+B, 131657 sq. ft., 2006)

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Page 12: 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

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

Page 13: 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

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

Page 14: 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

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Page 15: 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

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.

Page 16: 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

Campus-scale Energy Prediction

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(For the year 2010)

Page 17: 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

Building-scale Model

Training Data 17544 records

Test Data Separate test dataset for each building Each has 325 records

Page 18: 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

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

Page 19: 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

Building-scale evaluation

Page 20: 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

Building-scale Prediction

ASC (CV-RMSE = 12.03%)

Page 21: 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

Building-scale Prediction

EEB (CV-RMSE = 9.19%)

Page 22: 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

Future Work

Include fine-grained information in our model 15-min granularity energy use data Detailed occupancy data (classroom

assignments, course enrolment, room use)

Page 23: 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

Thanks

Page 24: 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

Academic Buildings (2009)

SAL – Salvatori CS (3F, 37521 sq. ft., 1976)

Comparison

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Page 25: 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

Residential Buildings (2009)

PTD – Pardee Tower (8F, 59209 sq. ft., 1982)

Comparison

Apr 21, 2023

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