Short‐term prediction of photovoltaic power generation based on neural network prediction model

Abstract Real‐time monitoring and accurate prediction of photovoltaic (PV) power generation operation parameters are essential to ensure stable operation. In this paper, a set online PV parameter measurement devices characterized by simple structure, high sampling accuracy, small data fluctuations, ease measurement, designed. Sensors based on the zero‐flux principle employed in real‐time collection output electrical signals process generation, realizing electric signals. Next, basic structure working cells analyzed, mathematical model for engineering purposes is established, wavelet neural network selected predict short‐term particle swarm optimization adding momentum used optimize weight (WNN) as well basis function. Finally, historical meteorological station taken training samples train simulate sub‐models different weather types verify effectiveness accuracy optimizing WNN algorithm. The research results paper can realize evaluation during PV‐power‐generation system.