TY - JOUR
N2 - At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
L1 - http://www.czasopisma.pan.pl/Content/128358/PDF/art04_int.pdf
L2 - http://www.czasopisma.pan.pl/Content/128358
PY - 2023
IS - No 3
EP - 628
DO - 10.24425/aee.2023.146040
KW - BP neural network
KW - photovoltaic power generation
KW - PSO–GWO model
KW - PSO–GWO–BP prediction model
KW - standard grey wolf algorithm
A1 - He, Ping
A1 - Dong, Jie
A1 - Wu, Xiaopeng
A1 - Yun, Lei
A1 - Yang, Hua
PB - Polish Academy of Sciences
VL - vol. 72
DA - 2023.09.11
T1 - Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
SP - 613
UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/128358
T2 - Archives of Electrical Engineering
ER -