Abstract
A short-term power load forecasting (STPLF) model based on the Improved Whale Optimization Algorithm (IWOA) optimized Kernel Extreme Learning Machine (KELM) is proposed to address the problems of high randomness and low forecasting accuracy of electricity loads. The KELM model is constructed, and the IWOA is used to optimize the core and penalty parameters of the KELM to establish the IWOA-KELM electricity load forecasting model. Combined with the actual data of a certain region, the forecasting analysis results show that the convergence speed and forecasting accuracy of the method are greatly improved compared with IWOA-BP, IWOA-SVM and IWOA-ELM forecasting methods.
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