光伏系统
人工神经网络
粒子群优化
主成分分析
计算机科学
功率(物理)
人工智能
算法
工程类
量子力学
电气工程
物理
作者
Kangzhuang Guo,Xingong Cheng,Jie Shi
出处
期刊:2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)
日期:2021-03-26
被引量:2
标识
DOI:10.1109/aeees51875.2021.9403046
摘要
The prediction of photovoltaic power generation is of great significance to safe and reliable operation of power grid. To solve the problem of low accuracy of photovoltaic power output prediction, this paper proposes a short-term power prediction method of photovoltaic power generation based on principal component analysis (PCA) and particle swarm optimization (PSO) neural network. Firstly, PCA is used to screen the original data to reduce the dimension and complexity of the data. Then, PSO is used to optimize the weights and thresholds of neural network, which makes up for the shortcomings of traditional BP neural network, such as long training time and easy to fall into local extreme points. The number of hidden layer nodes of neural network is determined by trichotomy, and the PSO-optimized neural network photovoltaic power generation output prediction model based on PCA is constructed. Finally, the actual photovoltaic power generation data and meteorological data are used for example analysis. The prediction error of the proposed model is reduced by 23.82%. The results show that compared with the previous model, the proposed model has more accurate photovoltaic output prediction under different weather types. It is reduced by 19.01%, 23.28% and 29.18% under sunny, cloudy and overcast weather conditions respectively, which verifies the effectiveness of the proposed method.
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