Building Lighting Energy Consumption Prediction for Supporting Energy Data Analytics. (2016)

• Record Type: Journal Article
• Authors: Amasyali, Kadir; El-Gohary, Nora
• Is Part Of: Procedia engineering. Volume 145(2016)
• Page Start: 511
• Page End: 517
• Publication Date: 2016
• Subjects: Data analytics -- Machine Learning -- Lighting energy consumption prediction -- Support vector machines.; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2016.04.036
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: 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.