Customer short term load forecasting by using ARIMA transfer function model

Abstract

Short-term load forecasting plays an important role in electric power system operation and planning. An accurate load forecasting not only reduces the generation cost in a power system, but also provides a good principle of effective operation. In this paper, the ARIMA model and transfer function model are applied to the short-term load forecasting by considering weather-load relationship. For four types of customer in Taiwan power (Taipower) system, residential load, commercial load, office load and industrial load customers, the summer ARIMA model transfer function model have been derived to proceed the short-term load forecasting during one week. To demonstrate the effectiveness of the proposed method, this paper compares the results of the transfer function model and the univariate ARIMA model with conventional regression. Besides, the transfer function model's accuracy of the load forecast on weekday and weekend is thoroughly investigated. To improve the accuracy level of load forecast, the temperature effect is considered in the transfer function. According to the short-term load forecasting for these four customer classes, it is concluded that the proposed method can achieve better accuracy of load forecast than the ARIMA model by considering the causality between power consumption and temperature.