Combination of Singular Spectrum Analysis and Autoregressive Model for Short Term Load Forecasting

One of the most important requirements for the operation and planning activities of an electrical utility is the prediction of load for the next hour to several days out, known as short term load forecasting (STLF). This paper presents a new method based on spectral analysis and autoregressive (AR) modeling that is capable to predict the electricity demand accurately. An AR model is optimized for each of the principle components obtained from singular spectrum analysis (SSA), and the multi step predicted values are recombined to make the load time series. This method is used for the STLF of Iran National Power System (INPS) and the performance of the method shows promising results for one hour up to a day prediction. The proposed method is comparable with intelligent methods.