基于CNN和LightGBM的新型风电功率预测模型

张爱枫; 段新宇; 何枭峰

张爱枫,段新宇,何枭峰.基于CNN和LightGBM的新型风电功率预测模型[J].电测与仪表,2021,58(11):121-127.
Zhang AIfeng,Duan Xiyu,He Xiaofeng.Short term wind power prediction based on convolution neural network and LightGBM algorithm[J].Electrical Measurement & Instrumentation,2021,58(11):121-127.

基于CNN和LightGBM的新型风电功率预测模型

Short term wind power prediction based on convolution neural network and LightGBM algorithm

DOI：10.19753/j.issn1001-1390.2021.11.017

中文关键词: 风力发电卷积神经网络 LightGBM 短期风电功率预测融合模型

英文关键词:wind power generation convolution neural network lightgbm short term wind power prediction fusion model

基金项目:

作者中文名	作者英文名	单位
张爱枫	Zhang AIfeng	重庆电力交易中心有限公司
段新宇	Duan Xiyu	国网湖南省电力有限公司电力科学研究院
何枭峰	He Xiaofeng	国电重庆恒泰发电有限公司

摘要点击次数: 2096

中文摘要:

考虑到风力发电的波动性和不确定性,本文提出了一种基于卷积神经网络和LightGBM相结合的新型风电功率预测模型。首先,通过分析风电场与相邻风电场原始数据的时序特征,构建出新的特征集；其次,应用卷积神经网络(CNN)从输入数据中提取信息,并通过比较实际结果调整网络参数；然后,考虑到单一卷积模型在预测风电时的局限性,将LightGBM分类算法集成到模型中从而提高预测的准确性和鲁棒性。最后,将提出的算法与已有的支持向量机、LightGBM和CNN进行仿真对比,结果证明所提出的融合模型具有更好的精度和效率。

英文摘要:

Considering the fluctuation and uncertainty of wind power generation, a new wind power prediction model based on convolution neural network and LightGBM is proposed in this paper. Firstly, a new feature set is constructed by analyzing the temporal characteristics of the original data of wind farms and adjacent wind farms. Secondly, the information is extracted from the input data by using convolution neural network (CNN) and the network parameters are adjusted by comparing the actual results. Then, considering the limitations of single convolution model in wind power prediction, LightGBM classification algorithm is integrated into the model to improve the prediction. Accuracy and robustness of measurement. Finally, the proposed algorithm is compared with the existing support vector machines, LightGBM and CNN. The results show that the proposed fusion model has better accuracy and efficiency.

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