Time series forecasting method of building energy consumption using support vector regression

Abstract

In this paper, we focus on the prediction method of building energy consumption time series. The building energy consumption data can be regarded as a time series, which is usually nonlinear and non-stationary. Traditional time series analysis model has lower prediction accuracy. Then the machine learning method, especially support vector regression algorithm always has better performance to deal with non-stationary and nonlinear time series. So the support vector regression algorithm is applied to develop building energy consumption time series model. The model is applied in different buildings. Experimental results show the prediction accuracy of the model is better than traditional time series analysis model.