Short‐term prediction of photovoltaic power generation based on neural network prediction model. Issue 1 (2nd October 2022)

• Record Type: Journal Article
• Authors: Chai, Mu; Liu, Zhenan; He, Kuanfang; Jiang, Mian
• Is Part Of: Energy science & engineering. Volume 11:Issue 1(2023)
• Page Start: 97
• Page End: 111
• Publication Date: 2022-10-02
• Subjects: particle swarm optimization -- PV power generation -- real‐time monitoring -- wavelet neural network -- zero flux; Energy industries -- Periodicals; Energy development -- Periodicals; Power resources -- Periodicals; 621.042
• DOI: 10.1002/ese3.1314
• Languages: English
• ISSNs: 2050-0505

Abstract

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