Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)

In this paper, the data analysis and short term load forecasting (STLF) in Iran electricity market has been considered. The proposed method is an improved singular spectral analysis (SSA) method. SSA decomposes a time series into its principal components i.e. its trend and oscillation components, which are then used for time series forecasting, effectively. The employed data are the total load time series of Iran electricity market in its real size and is long enough to make it possible to take properties such as non-stationary and annual periodicity of the market into account. Simulation results show that the proposed method has a good ability in characterizing and prediction of the desired load time series in comparison with some other related methods.

► Short term load forecasting (STLF) of Iran electricity market is considered.
► The real and long duration of data have been analyzed using singular spectral analysis (SSA).
► An improved SSA method has been proposed for STLF.
► The omission of cumulative forecasting error and non-stationarity of the data has been considered for improvement of SSA.
► The proposed method has been compared with two other methods to show its good performance.