基于改进FCM-LSTM的光伏出力短期预测研究  

STUDY ON SHORT-TERM PHOTOVOLTAIC OUTPUT PREDICTION BASED ON IMPROVED FCM-LSTM

作　　者：秦宇 许野 王鑫鹏 王涛 李薇 Qin Yu;Xu Ye;Wang Xinpeng;Wang Tao;Li Wei(College of Environmental Science and Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学环境科学与工程学院,北京102206

出　　处：《太阳能学报》2024年第8期304-313,共10页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(62073134)。

摘　　要：受制于外界气象条件和设备性能损失等多方面因素的影响,光伏电站的发电功率呈现出很强的波动性和随机性,精确的光伏出力预测对光伏电站的运营管理和电网的调度运行至关重要。针对传统模糊C均值聚类算法(FCM)无法自主确定聚类数以及欧氏距离在高维数据分类上的不足,在传统FCM的基础上引入自适应因子和加入余弦距离作为样本分类指标,确定与待预测数据相似程度最高的历史样本簇集,创新性地提出一种基于改进FCM和长短期记忆(LSTM)神经网络的短期光伏出力组合预测模型。在云南某光伏电站的应用结果显示,对比其他预测模型,所提方法的历史样本分类效果更佳,发电功率预测精度更高,验证了该方法的有效性与优越性。The electricity generation of photovoltaic power plant is affected by various factors such as external weather condition and equipment performance loss,showing the strong volatility and stochasticity.Therefore,the accurate prediction of PV output is crucial to the operation and management of PV power plant and the scheduling operation of power grid.To solve the shortcomings of traditional fuzzy C-means clustering(FCM)that cannot determine the number of clusters autonomously and the Euclidean distance in classifying high-dimensional data,in this study,the adaptive factor and cosine distance as the sample classification index are integrated with traditional FCM,leading to the set of historical sample clusters with the highest similarity to the data to be predicted.Finally,a shortterm PV output portfolio prediction model based on improved FCM and long-short term memory(LSTM)neural network is innovatively established.The applied results in a PV plant in Yunnan show that the proposed method has the better classification of historical samples and the higher prediction accuracy of power generation than other prediction models,which verifies the effectiveness and superiority of the method.

关 键 词：光伏出力短期预测 模糊C均值聚类 自适应方法 余弦距离 长短期记忆神经网络 

分 类 号：TM615[电气工程—电力系统及自动化]