基于长短记忆神经网络优化的短期光伏发电预测方法
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引用本文:孟巍,郭腾炫,刘昳娟,张东宁,宗振国.基于长短记忆神经网络优化的短期光伏发电预测方法[J].电网与清洁能源,2022,38(5):129~134
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作者单位
孟巍 国网山东省电力公司 
郭腾炫 国网山东省电力公司 
刘昳娟 国网山东省电力公司 
张东宁 国网山东省电力公司 
宗振国 国网山东省电力公司 
基金项目:国网山东省电力公司科技项目(2020A-009)
中文摘要:针对现阶段光伏发电输出功率不稳定和发电预测模型实施难度较大的问题,对基于长短记忆神经网络优化的短期光伏发电预测方法进行研究。通过分析神经网络分布特点,在数据优化模型中代入初始数据,不断迭代计算目标权重,引入自循环乘积法获取模型的最佳优化函数;通过待预测数据之间的类间距计算可分性,将数据划分为对比序列和参考序列,分析参考序列内每个单位时刻下数据的类簇关联度,根据关联度量化值提取下一时刻的数据权重,完成短期光伏发电数据的预测。仿真实验表明,所提方法的预测精度高,该模型结构直观、易实施,对数据包容性强,可以高效实现对发电数据的预测。
中文关键词:目标权重  自循环乘积法  优化函数  类簇关联度  量化预测
 
A Prediction Method for Short-Term Photovoltaic Power Generation Based on Short-Length Memory Neural Network Optimization
Abstract:In view of the unstable output power of photovoltaic power generation and the difficulty of implementing the power generation prediction model at the present stage, the short-term photovoltaic power generation prediction method based on long and short memory neural network optimization is studied. By analyzing the distribution characteristics of neural network, the initial data is substituted into the data optimization model, the target weight is calculated iteratively, and the self-cyclic product method is introduced to obtain the optimal optimization function of the model. The separability is calculated through the class spacing between the data to be predicted, the data is divided into comparison sequence and reference sequence, the class cluster correlation degree of the data at each unit time in the reference sequence is analyzed, and the data weight at the next time is extracted according to the quantitative value of correlation degree so that the prediction of short-term photovoltaic power generation data is completed. Simulation results show that the proposed method has high prediction accuracy, intuitive model structure, easy implementation, strong data inclusiveness, and can effectively realize power generation data prediction.
keywords:target weight  self-cyclic product method  optimization function  cluster correlation degree  quantitative prediction
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