TY - JOUR
T1 - Building Lighting Energy Consumption Prediction for Supporting Energy Data Analytics
AU - Amasyali, Kadir
AU - El-Gohary, Nora
N1 - Funding Information:
The authors would like to thank the Qatar Foundation. This material is based upon work supported by the National Priorities Research Program (NPRP) of the Qatar National Research Fund (QNRF) under Grant No. NPRP 6-1370-2-552. The authors would also like to thank the Philadelphia Business and Technology Center (PBTC) and the Penn State Consortium for Building Energy Innovation (CBEI) for providing access to building energy data.
Publisher Copyright:
© 2016 The Authors.
PY - 2016
Y1 - 2016
N2 - Recent studies emphasized the importance of building energy consumption prediction for improved decision making. Data-driven models are being widely used for building energy consumption prediction. Among these, support vector machines (SVM) gained a lot of popularity due to its capability of handling non-linear problems. This paper presents an SVM-based lighting energy consumption prediction model for office buildings. For this study, an office building in Philadelphia, PA was instrumented and the required lighting energy consumption data to train the model were collected from this building. The developed model predicts daily lighting energy consumption based on two features: daily average sky cover and day type. The results showed that the developed model could be a good baseline model for predicting lighting energy consumption, which could be further extended by taking occupant behavior into account.
AB - Recent studies emphasized the importance of building energy consumption prediction for improved decision making. Data-driven models are being widely used for building energy consumption prediction. Among these, support vector machines (SVM) gained a lot of popularity due to its capability of handling non-linear problems. This paper presents an SVM-based lighting energy consumption prediction model for office buildings. For this study, an office building in Philadelphia, PA was instrumented and the required lighting energy consumption data to train the model were collected from this building. The developed model predicts daily lighting energy consumption based on two features: daily average sky cover and day type. The results showed that the developed model could be a good baseline model for predicting lighting energy consumption, which could be further extended by taking occupant behavior into account.
KW - Data analytics
KW - Lighting energy consumption prediction
KW - Machine Learning
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=84999752024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84999752024&partnerID=8YFLogxK
U2 - 10.1016/j.proeng.2016.04.036
DO - 10.1016/j.proeng.2016.04.036
M3 - Conference article
AN - SCOPUS:84999752024
SN - 1877-7058
VL - 145
SP - 511
EP - 517
JO - Procedia Engineering
JF - Procedia Engineering
T2 - International Conference on Sustainable Design, Engineering and Construction, ICSDEC 2016
Y2 - 18 May 2016 through 20 May 2016
ER -