基于CNN和LightGBM的新型风电功率预测模型

A new wind power prediction model based on CNN and LightGBM

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.