为提高光伏发电系统输出功率的预测精度,提出一种基于皮尔逊插值算法（PIA）、模糊C均值聚类算法（FCM）、多元变分模态分解（MVMD）、卷积神经网络（CNN）、双向长短期记忆网络（BiLSTM）和注意力机制（ATTENTION）的短期光伏功率预测组合模型。首先,利用PIA对光伏电站采集的原始数据进行修复;其次,FCM算法将历史数据聚类为晴天、阴天、雨天;然后,通过MVMD对光伏功率进行分解,得到若干本征模态函数;接着,采用CNN和BiLSTM网络相结合充分提取各本征模态函数的特征,同时引入注意力机制以突出重要信息并赋予其权重;最后,对各本征模态函数预测,将各预测值叠加得到最终预测结果,与其他光伏功率预测模型对比验证所提混合模型具有更好的预测精度。
Abstract
To improve the accuracy of photovoltaic （PV） power prediction,a hybrid short-term photovoltaic power prediction model based on the Pearson interpolation algorithm （PIA）,fuzzy C-means clustering algorithm （FCM）,multivariate variational mode decomposition （MVMD）,convolutional neural network （CNN）,bidirectional long short-term memory network （BiLSTM）,and attention mechanism （ATTENTION） is proposed.Firstly,the PIA is utilized to repair the missing data in the dataset. Secondly,the FCM algorithm clusters historical data into sunny,cloudy,and rainy days. Then,the MVMD is employed to decompose the PV power into several intrinsic mode functions （IMFs）.Subsequently,a combination of CNN and BiLSTM networks is used to fully extract the features of each IMF,with the Attention Mechanism introduced to emphasize and weigh important information. Finally, the forecasting of each IMF is conducted,and the predicted values are summed to obtain the final prediction result. Compared with other PV power forecasting models,the proposed hybrid model demonstrates superior forecasting accuracy.
关键词
光伏功率 /
预测模型 /
多元变分模态分解 /
模糊聚类 /
双向长短期记忆神经网络 /
注意力机制
Key words
photovoltaic power /
prediction model /
multivariate variational mode decomposition /
fuzzy clustering /
bidirectional long short-term memory neural network /
attention mechanism
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基金
国家重点研发计划（2022YFA1004600）
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