Hybrid method for building energy consumption prediction based on limited data

The blossoming of building related data has led to the rapid development of machine learning methods in building energy consumption prediction. This has also allowed for the strengths and brilliance of machine learning methods over popular statistical methods such as seasonal autoregressive integrated moving average (SARIMA) to be exposed. However, for some old buildings that cannot provide sufficient data, it would be intractable and inefficient to apply machine learning methods to predict energy consumption. In this study, a hybrid method based on SARIMA and support vector machine (SVM) was proposed to predict the energy consumption of a relatively old educational building that solely had electricity consumption data. The performance of proposed method was compared with SARIMA. The results showed that SARIMA accurately captured and predicted linear aspects of the building energy. Although SVM is proficient for capturing inherent non-linearity within limited data, the lack of input variables such as occupant behaviours often restrict SVM accuracy. Multiple comparisons between 1-year and 2-year training data indicated that extending time spans of training data only marginally improves prediction performance. In this study, the accuracy was impeded by lack of adequate information about the building closure during festive periods.