摘要
由于光伏发电的波动性和随机性,传统模型难以对其进行准确预测。针对这一问题,在聚类的基础上建立一种自适应权重的CNN-LSTM网络模型。首先,对光伏电站历史数据进行预处理,并采用基于肘部法的K-means算法进行聚类;其次,根据同聚类簇的训练样本与对应测试样本的特征中心间的距离建立自适应权重;然后,根据聚类结果和自适应权重建立适用于不同聚类类别的AW-CNN-LSTM网络模型,其中,CNN用于捕获不同特征间的关系,LSTM用于捕获时序特征;最后对各模型预测结果进行整合得到最终预测结果。在澳大利亚沙漠太阳能研究中心的光伏电站数据集上进行试验,证明了本文所提方法的有效性。
Due to the volatility and randomness of photovoltaic power generation,it is difficult for traditional models to accurately predict it.To solve this problem,a prediction model of AW-CNN-LSTM is established based on clustering.First,the photovoltaic power plant historical data set is pre-processed and clustered using the K-means clustering algorithm based on the elbow method;secondly,an adaptive weight is established based on the distance between the training samples and the feature center of test samples of the same clustering category;then,an AW-CNN-LSTM network model suitable for different clustering categories is established based on the clustering results and adaptive weights.CNN are used to capture the relationships between different features,while LSTM are used to capture temporal features.Finally,the forecast results of each model are integrated to get the final forecast results.Experiments on the data set of photovoltaic power stations in the Australian Desert Solar Energy Research Center demonstrate the effectiveness of the proposed method.
作者
刘丽丽
谢梦
王艳
杨春蕾
顾明剑
Liu Lili;Xie Meng;Wang Yan;Yang Chunlei;Gu Mingjian(Suzhou Institute of Science and Technology Physics,Suzhou 215000,China;Yunyao Power Technology(Suzhou)Co.,Ltd.,Suzhou 215000,China)
出处
《电子测量技术》
北大核心
2025年第18期92-99,共8页
Electronic Measurement Technology
基金
苏州市科技项目(SYG202135)
苏州市社会发展项目(2023ss17)资助。
关键词
光伏功率预测
自适应权重
K-MEANS聚类
卷积神经网络
长短期记忆网络
photovoltaic power prediction
adaptive weight
K-means clustering
convolutional neural networks
long short term memory networks
