Short-term load forecasting using regression based moving windows with adjustable window-sizes

This paper presents a regression based moving window model for solving the short-term electricity forecasting problem. Moving window approach is employed to trace the demand pattern based on the past history of load and weather data. Regression equation is then formed and least square method is used to determine the parameters of the model. In this paper, a new concept associated with cooling and heating degree is used to establish the relationship between electricity demand and temperature, which is one of the key climatic variables. In addition, Pearson's correlation has been employed to investigate the interdependency of electricity demand between different time periods. These analyses together with the data in the holiday period provide the supportive information for the appropriate selection of the window size. A case study has been reported in this paper by acquiring the relevant data for the state of New South Wales, Australia. The results are then compared with a neural network based model. The comparison shows that the proposed moving window approach with the different window sizes outperforms conventional neural network technique in small time scales i.e., from 30 minutes to 1 day ahead.