Hyperparameter optimization of support vector machine using adaptive differential evolution for electricity load forecasting

Peak load forecasting plays an integral part in the planning and operating of energy plants for the utility companies and policymakers to devise reliable and stable power infrastructure. However, the electricity load profile is considered a complex signal due to the non-linear and stochastic behavior of the consumer. Therefore, a rigid forecasting model with assertive stochastic and non-linear behavior capturing abilities is required to estimate the demand capacity accurately. To handle these uncertainties, this paper proposed a hybrid model that integrates the multivariate empirical modal decomposition (MEMD) and adaptive differential evolution (ADE) algorithm with a support vector machine (SVM). MEMD allows the decomposition of multivariate data to deteriorate over time to effectively extract the unique information from exogenous variables over different time frequencies to ensure high computational efficiency. The ADE algorithm obtains and tunes the SVM model's appropriate parameters to effectively avoid trapping into local optimum and returns accurate forecasting results. Consequently, the proposed MEMD-ADE-SVM forecasting model simultaneously achieves good accuracy (93.145%), stability, and convergence rate, respectively. A historical load dataset from the independent system operator (ISO) New England (ISO-NE) energy sector is analyzed to verify the MEMD-ADE-SVM hybrid model. The results show that the developed MEMD-ADE-SVM model outperforms the benchmark frameworks such as; SVR-based model by hybridizing variational mode decomposition, the chaotic mapping mechanism, and the grey wolf optimizer (VMD-SVR-CGWO), SVM based on data preprocessing and whale optimization algorithm (DCP-SVM-WO), intelligent optimized SVR model based on variational mode decomposition and Fast Fourier transform (VMD-FFT-IOSVR), SVR model based on multivariate empirical mode decomposition and particle swarm optimization (EMD-SVR-PSO), and MEMD-ADE-LSTM for day-ahead and week-ahead electricity peak load forecasting in terms of accuracy, stability, and convergence rate.(c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).