Using Support Vector Machine to Predict Next Day Electricity Load of Public Buildings with Sub-metering Devices

Abstract

Predicting short term electricity load accurately is critical to facilitate demand side management in the building sector. For buildings that have electricity sub-metering systems installed, it is possible to predict both the total electricity load and the loads of individual building service systems (air conditioning, lighting, power, and other equipment). In this paper, a method based on Support Vector Machine (SVM) is proposed to predict the loads at system level. For each type of system, 24 SVM models (one model per hour) were trained and deployed to predict the hourly electricity load. The inputs for the prediction method are simply weather predictions and hourly electricity loads in two previous days. A case study shows that the proposed method outperforms three other popular data mining methods (ARIMAX, Decision Tree, and Artificial Neural Network) in both CV_RMSE and N_MBE. Thus, SVM method is suggested for predicting system level electricity loads of public buildings.