南京师范大学学报（工程技术版）2024，Vol.24Issue(2)：11-19,9.DOI:10.3969/j.issn.1672-1292.2024.02.002
基于优化小波变换神经网络的分布式新能源信息预测方法
Optimized Wavelet Transform Neural Networks for Accurat Distributed Renewable Energy Information Prediction
摘要
Abstract
Distributed renewable energy generation is a crucial component of low-carbon power systems.As the proportion of distributed renewable energy in urban power grids is gradually increasing,and the impacts of random load fluctuations and random weather changes on urban power grids are increasing,placing higher demands on the accuracy of distributed renewable energy information forecasting.Currently,the primary generation methods of distributed renewable energy are distributed photovoltaic power generation and distributed wind power generation.The changes of urban electricity load are both cyclical and random,while factors such as wind speed and solar irradiance have significant impacts on distributed wind power generation and distributed photovoltaic power generation,respectively.Therefore,based on wavelet transform neural network,a distributed renewable energy information prediction method is constructed.Firstly,the model of distributed renewable energy is established by analyzing the working principle of distributed renewable energy.Then,the wavelet transform neural network is optimized to predict the parameters that play a significant role in the renewable energy grid,such as the load power change and the irradiation intensity,using wind power generation and photovoltaic power generation as examples.Finally,the example verifies that the proposed model can accurately predict the information of distributed renewable energy.关键词
分布式新能源/负荷预测/辐照强度预测/城市电网/小波变换神经网络Key words
distributed renewable energy/load prediction/irradiation intensity prediction/urban power grid/wavelet transform neural network分类
信息技术与安全科学引用本文复制引用
栾开宁,庄重,杨世海,段梅梅,孔月萍,周雨奇,张汀荃,丁泽诚..基于优化小波变换神经网络的分布式新能源信息预测方法[J].南京师范大学学报（工程技术版）,2024,24(2):11-19,9.基金项目
国家电网有限公司科技项目(J2022045). （J2022045）
