Generalization of solar power yield modeling using knowledge transfer

作者：

Highlights：

• A novel weighting technique for support vector machine to reduce negative transfer.

• Proposed a Transfer Support Vector Regression (Tr-SVR) model powered by weighted-SVM.

• Validated the proposed model both theoretically and empirically using four datasets.

• Presented a comparative study between the proposed model with baseline models.

摘要

•A novel weighting technique for support vector machine to reduce negative transfer.•Proposed a Transfer Support Vector Regression (Tr-SVR) model powered by weighted-SVM.•Validated the proposed model both theoretically and empirically using four datasets.•Presented a comparative study between the proposed model with baseline models.

论文关键词：Energy forecasting,Knowledge transfer,Photovoltaic,Transfer learning,Machine learning

论文评审过程：Received 6 May 2021, Revised 23 February 2022, Accepted 25 March 2022, Available online 4 April 2022, Version of Record 19 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116992