A hybrid forecasting model for short-term power load based on sample entropy, two-phase decomposition and whale algorithm optimized support vector regression

Abstract

To improve the accuracy and reliability of short-term power load forecasting and reduce the difficulty caused by load volatility and non-linearity, a hybrid forecasting model (CEEMDAN-SE-VMDPSR- WOA-SVR) is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to generate multiple intrinsic modal functions (IMF) by decomposing the historical power load series. Then the sample entropy (SE) of each IMF is calculated to quantitatively evaluate the corresponding complexity. Afterward, variational mode decomposition (VMD) is adopted to achieve secondary decomposition for the component with the maximum sample entropy. Subsequently, the phase space reconstruction (PSR) is applied to reconstruct each IMF into a high-dimensional feature space matrix, which is formed as the input of support vector regression (SVR). Finally, SVR optimized by whale optimization algorithm (WOA) is used for the prediction, where the predicted values of all IMFs are accumulated to obtain the final prediction results. The experimental result demonstrates that the proposed hybrid model can effectively decompose the load series with non-linear characteristic and provide more accurate forecasting results by comparing the other models.