Building Lighting Energy Consumption Prediction for Supporting Energy Data Analytics

Kadir Amasyali, Nora El-Gohary

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)511-517
Number of pages7
JournalProcedia Engineering
StatePublished - 2016
EventInternational Conference on Sustainable Design, Engineering and Construction, ICSDEC 2016 - Tempe, United States
Duration: May 18 2016May 20 2016


  • Data analytics
  • Lighting energy consumption prediction
  • Machine Learning
  • Support vector machines

ASJC Scopus subject areas

  • Engineering(all)


Dive into the research topics of 'Building Lighting Energy Consumption Prediction for Supporting Energy Data Analytics'. Together they form a unique fingerprint.

Cite this