Short Term Load Forecasting Using Empirical Mode Decomposition, Wavelet Transform and Support Vector Regression

The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, the intrinsic mode functions (IMFs) of the Electric load curve, which are a group of average and pseudo-periodic average signals, are extracted by using the empirical mode decomposition (EMD) method, which is a non-linear and non-constant time-frequency method. For this purpose, the maximum and minimum points of the signal are determined, and then, in one cycle, the difference between the average curve of the upper and lower envelope is calculated with it. This continues until the result falls below a threshold value, and then, the rest of the signal which contains noise is discarded to get a relatively clean signal. In the second step, we need to obtain the sub-sequences of each IMF. So, we use the wavelet transform. The wavelet transform is a kind of transform that is used to decompose a continuous signal into its frequency components, and the resolution of each component is equal to its scale. Each subsequence contains different information and details that can help the improvement of the prediction accuracy. In the third step, the obtained subsequences are aggregated and finally used for prediction by Support Vector Regression (SVR). Support vector regression is a type of supervised learning system that is used for both grouping and estimating the fitting function of data in regression problems so that the least error occurs in the grouping of data or in the fitting function. The purpose of the proposed method is to reduce the error for daily and hourly load prediction. In this method, two datasets of Poland and Canada have been experimented. With four criteria of mean square error (MSE), root mean square error (RMSE), average absolute percentage error (MAPE) and mean absolute error (MAE), the results are evaluated. The findings show that the load prediction error for the Polish data set are as follows: MSE equal to 0.0012, RMSE equal to 0.0342, MAPE equal to 2.9771, and MAE equal to 0.0044. For Canadian data set, the results are as follows: MSE equal to 5.0969e-07, RMSE equal to 7.1393e-04, MAPE criterion equal to 0.9571, and the MAE criterion equal to 2624e-04. Comparison of the proposed method with other competing methods show that better results are achieved by the proposed method in term of the error rate.