Multivariate Adaptive Step Fruit Fly Optimization Algorithm Optimized Generalized Regression Neural Network for Short-Term Power Load Forecasting - Jiang, Feng; Zhang, Wenya; Peng, Zijun (Frontiers in Environmental Science, 2022)
Short-term load forecasting plays a significant role in the management of power plans. In this paper, we propose a multivariate adaptive step fruit fly optimization algorithm (MAFOA) to optimize the smoothing parameter of the generalized regression neural network (GRNN) in the short-term power load forecasting. In addition, due to the substantial impact of some external factors including temperature, weather types and date types on the short-term power load, we take these factors into account and propose an efficient interval partition technique to handle the unstructured data. To verify the performance of MAFOA-GRNN, the power load data are used for empirical analysis in Wuhan city, China. The empirical results demonstrate that the forecasting accuracy of MAFOA applying to GRNN outperforms the benchmark methods.