AUTHOR=Jiang Feng , Zhang Wenya , Peng Zijun 

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

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.873939

DOI=10.3389/fenvs.2022.873939

ISSN=2296-665X

ABSTRACT=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.