关键词: 自回归移动平均, 支持向量机, 空调冷负荷

Abstract: For industrial users, the prediction of air-conditioning load can be great helpful to reduce the energy consumption. Besides the nonlinear character, the user's load data is also very sensitive to the external disturbance and with daily periodic characteristics. This makes the traditional ARMA and SVR methods hard to achieve good prediction. Therefore, a method of combining ARMA model and SVR model with daily periodic characteristics is presented. Firstly, combined with the daily periodic characteristic of the raw data, the ARMA model is used for linear prediction. For the nonlinear characteristics of original data which retained in the prediction residuals of ARMA model, the SVR model is used to predict the nonlinear part of the residuals and modify the prediction result, obtaining the final predicted value. Experimental results using actual data show that the proposed method can significantly improve the prediction performance.

Key words: Auto-regressive moving average, support vector machine; , air-conditioning cooling load