Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning.

Increasing the accuracy and intelligence of short-term load forecasting system can improve modern power systems management and economic power generation. In recent decades, the optimized machine learning methods have been widely used in load forecasting problems because of their predictability with higher accuracy and robustness. However, most related researches only use evolutionary algorithms for parameters fine-tuning and ignore the evolutionary algorithm based decision-making support and the matching relation between the used evolutionary algorithm and machine learning method, which greatly limit the improvement of forecasting system. To dissolve the above issues, a data-driven evolutionary ensemble learning forecasting model is proposed in this paper. Firstly, a novel multimodal evolutionary algorithm based on comprehensive weighted vector angle and shift-based density estimation is proposed. Secondly, based on the proposed multimodal evolutionary algorithm, an intelligent decision-making support scheme including predictive performance evaluation, model properties analysis, structure and fusion strategy optimization, and optimal model preference selection is designed to improve the random vector functional link network based ensemble learning model and boost the forecasting accuracy. Thirdly, experimental studies on 15 test problems with up to 6000 decision variables are conducted to validate the excellent optimization ability of the proposed evolutionary algorithm. Finally, the proposed evolutionary ensemble learning method is compared with 6 other representative forecast methods on real-world short-term load forecasting datasets from Australia, Great Britain, and Norway. The experiment results verify the superiority and applicability of the proposed method. • A new multimodal evolutionary algorithm (MMOWVA) is being proposed. • MMOWVA is validated by comparison experiments on 15 multimodal problems. • An expandable intelligent scheme is offered to make decision-makers convenient. • An evolutionary ensemble learning model is designed to improve forecast accuracy. • Experiment results demonstrate a higher forecast accuracy of the proposed method. [ABSTRACT FROM AUTHOR]