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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 …