基于改进Stacking与误差修正的短期太阳辐照度预测
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1.湖北工业大学;2.湖北工业大学 电气与电子工程学院

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TP391;TM615 ????????????? ?????????????

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国家重点研发计划项目(2018YFC0116100);湖北省重点研发计划项目(2020BAB114);湖北省教育厅科学研究计划重点项目(D20211402)。


Short-Term Solar Irradiance Prediction Based on Improved Stacking and Error Correction
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1.Hubei University of Technology;2.School of Electrical and Electronic Engineering,Hubei University of Technology

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    摘要:

    为提高光伏电站辐照度预测的准确性和可靠性,该文提出一种基于改进Stacking集成学习与误差修正的短期辐照度预测模型。首先,利用梯度提升决策树(gradient boosting decision tree,GBDT)对原始数据集进行特征筛选,清除冗余特征,提高预测精度和运算效率;然后建立改进Stacking辐照度预测模型,根据K折交叉验证下初级层不同预测模型所表现出预测准确度的差异性,对预测结果进行赋权,并对Stacking第一层输入第二层的训练集数据采用Box-Cox变换处理,以提高预测的正态性和同方差性。最后提取历史预测误差数据,采用随机森林(random forest,RF)构造误差模型,进一步提高预测精度。实验结果表明,该模型相比传统模型和经典Stacking模型其预测性能有了较大的提升。

    Abstract:

    Aiming at increasing the accuracy and reliability of irradiance prediction, the paper proposes a prediction model of short-term irradiance based on improved Stacking ensemble learning and error correction. Firstly, through gradient boosting decision tree, the paper characteristically screens the original data set, removes redundant characteristics, and increases prediction accuracy and computing efficiency; then, an improved Stacking irradiance prediction model is established. In accordance with difference in prediction accuracy of different prediction models in the primary layer under K-fold cross-validation, the paper weights the prediction results, and applies Box-Cox to transform and process the training set data input from the first layer to second layer of Stacking, so as to increase the normality and homoscedasticity of prediction. Finally, the paper extracts the historical prediction error data, and applies random forest to construct an error model to further increase the prediction accuracy. Comparing with the traditional model and the classic Stacking model, the experimental results show that there are significantly improvements on the prediction performance of model.

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王珊珊,吴 霓,何嘉文,朱 威.基于改进Stacking与误差修正的短期太阳辐照度预测[J].南京信息工程大学学报,,():

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  • 收稿日期:2023-02-10
  • 最后修改日期:2023-03-23
  • 录用日期:2023-03-24
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