Multivariate Adaptive Step Fruit Fly Optimization Algorithm Optimized Generalized Regression Neural Network for Short-Term Power Load Forecasting

Short-term load forecasting plays a significant role in the management of power plants. In this paper, we propose multivariate adaptive step fruit fly optimization algorithm (MAFOA) to optimize smoothing parameter generalized regression neural network (GRNN) short-term forecasting. addition, due substantial impact some external factors including temperature, weather types, and date types on load, take these into account an efficient interval partition technique handle unstructured data. To verify performance MAFOA-GRNN, data are used for empirical analysis Wuhan City, China. The results demonstrate that accuracy MAFOA applied GRNN outperforms benchmark methods.