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-VMD-PSR-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. INDEX TERMS short-term power load forecasting, two-phase decomposition, sample entropy, whale optimization algorithm, support vector regression
Precise and reliable hydrological runoff prediction plays a significant role in the optimal management of hydropower resources. Nevertheless, the hydrological runoff practically possesses a nonlinear dynamics, and constructing appropriate runoff prediction models to deal with the nonlinearity is a challenging task. To overcome this difficulty, this paper proposes a three-stage novel hybrid model, namely, CVS (CEEMDAN-VMD-SVM), by coupling the support vector machine (SVM) with a two-stage signal decomposition methodology, combining complete ensemble empirical decomposition with additive noise (CEEMDAN) and variational mode decomposition (VMD), to obtain inclusive information of the runoff time series. Hydrological runoff data of the Swat River, Pakistan, from 1961 to 2015 were taken for prediction. CEEMDAN decomposes the runoff time series into subcomponents, and VMD performs further decomposition of the high-frequency component obtained after CEEMDAN decomposition to improve the prediction activity. Afterward, the SVM algorithm was applied to the decomposed subcomponents for the prediction purpose. Finally, four statistical indices are utilized to measure the performance of the CVS model compared with other hybrid models including CEEMDAN-VMD-MLP (multilayer perceptron), CEEMDAN-SVM, VMD-SVM, CEEMDAN-MLP, VMD-MLP, SVM, and MLP. The CVS model performs better during the training period by reducing RMSE by 71.28% and 40.06% compared with MLP and CEEDMAD-VMD-SVM models, respectively. However, during the testing period, the error reductions include RMSE by 68.37% and 35.33% compared with MLP and CEEDMAD-VMD-SVM models, respectively. The results highlight that the CVS model outperforms other models in terms of accuracy and error reduction. The research also highlights the superiority of other hybrid models over standalone in predicting the hydrological runoff. Therefore, the proposed hybrid model is applicable for the nonlinear features of runoff time series with feasibility for future planning and management of water resources.
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