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作 者:张爱枫 段新宇 何枭峰 Zhang Aifeng;Duan Xinyu;He Xiaofeng(Chongqing Electric Power Trading Center Co.,Lid.,Chongqing 404100,China;Electric Power Research Institute,State Grid Hunan Electric Power Co.,Ltd.,Changsha 410000,China;Guodian Chongqing Hengtai Power Generation Co.,Ltd.,Chongqing 400053,China)
机构地区:[1]重庆电力交易中心有限公司,重庆404100 [2]国网湖南省电力有限公司电力科学研究院,长沙410000 [3]国电重庆恒泰发电有限公司,重庆400053
出 处:《电测与仪表》2021年第11期121-127,共7页Electrical Measurement & Instrumentation
摘 要:考虑到风力发电具有波动和不确定的特点,难以预测,文章提出了基于卷积神经网络和LightGBM算法相结合的新型风电功率预测模型。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;应用卷积神经网络(CNN)从输入数据中提取信息,基于数据间的对比结果调整相应参数;为了提高预测结果的准确性和鲁棒性,将LightGBM分类算法加入模型中。对比所提模型与支持向量机以及单一的LightGBM和CNN模型仿真结果,证明所提模型具有更好的精度和相率。Considering the fluctuation and uncertainty of wind power generation, which is hard to predict, 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. Then 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. To improve the accuracy and robustness of the prediction results, the LightGBM classification algorithm is added to the model. The proposed algorithm is compared with the existing support vector machines, LightGBM and CNN, showing that the proposed fusion model has better accuracy and efficiency.
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
分 类 号:TM715[电气工程—电力系统及自动化]
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