基于优化的模糊神经模型的光伏发电预测的研究  

Forecast of Photovoltaic Power Generation Based on Optimized Fuzzy Neural Model

作　　者：沈璐璐[1] SHEN Lulu(Basic Course Teaching Department, Shanxi Energy Institute, Xianyang 712000, China)

机构地区：[1]陕西能源职业技术学院基础课教学部,陕西咸阳712000

出　　处：《微型电脑应用》2020年第9期109-113,共5页Microcomputer Applications

摘　　要：采用模糊神经网络模型实现对不同环境条件下光伏发电系统电气特性进行预测,并使用粒子群优化算法对模糊神经模型地结构和参数进行优化训练。实验证明所提出的优化模糊神经模型具有更优的准确性、紧凑性和可解释性,可以在线估计和预测单个光伏模块电气特性以及最大功率点,且具有较好的计算性能。In this paper,the fuzzy neural network model is used to predict the electrical characteristics of the photovoltaic power generation system under different environmental conditions,and the particle swarm optimization algorithm is used to optimize the structure and parameters of the fuzzy neural model.Experiments prove that the proposed optimized fuzzy neural model has better accuracy,compactness and interpretability.It can estimate and predict the electrical characteristics and maximum power point of a single photovoltaic module online,and it has good computing performance.

关 键 词：光伏发电预测 模糊神经网络模型 粒子群优化 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]