A clustering-based short-term load forecasting using independent component analysis and multi-scale decomposition transform

A short-term electrical load forecasting model is proposed in this work. The proposed model is based on independent component analysis (ICA), discrete wavelet transform, Clustering, analysis of variance (ANOVA), and support vector regression (SVR) (called as IDCA-SVR). In the first step, ICA is used to decompose the given signal to its basic components. In the second step, a multi-scale decomposition is applied to each component to transform it into the corresponding subsequences. In the third step, all subsequences are clustered by utilizing the K-means clustering algorithm. Then, in the fourth step, the ANOVA test is applied to each cluster. The cluster with the highest intra-cluster correlation is selected. Finally, the electrical load is forecasted using SVR. The proposed IDCA-SVR method generates the most informative subsequences of the load time series with the lowest noise and outliers containing multi-scale information with both high and low frequencies. The proposed method is evaluated on three datasets of Poland, Bosnia and Herzegovina, and Canada (AMPds). The results show that the proposed method significantly decreases the forecasting error. Different measures such as mean absolute percent error (MAPE) and mean square error (MSE) are used to evaluate the forecasting results. According to done experiments, the proposed method forecasts the electrical load of Poland (training dataset) with MAPE = 1.7113 and MSE = 3.4621e-04, Poland (testing dataset) with MAPE = 2.3382 and MSE = 6.5692e-04, Bosnia and Herzegovina with MAPE = 1.8475e-04 and MSE = 1.0742e-09, and AMPds with MAPE = 0.499 and MSE = 1.4335e-07. According to the evaluation results, the proposed method works well on three datasets of Poland, Bosnia and AMPds compared to other forecasting methods.