Hybrid machine learning forecasting of solar radiation values

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• 作者：Yvonne Gala^a ; ^{yvonne.gala@estudiante.uam.es" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Á ; ngela Ferná ; ndez^a ; ^{a.fernandez@uam.es" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Julia Dí ; az^b ; ^{julia.diaz@iic.uam.es" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; José ; R. Dorronsoro^a ; ^{jose.dorronsoro@uam.es" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Solar radiation ; Support Vector Regression ; Gradient Boosting ; Random Forests ; Numerical Weather Prediction
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：2 February 2016
• 年：2016
• 卷：176
• 期：Complete
• 页码：48-59
• 全文大小：1298 K

文摘

The constant expansion of solar energy has made the accurate forecasting of radiation an important issue. In this work we apply Support Vector Regression (SVR), Gradient Boosted Regression (GBR), Random Forest Regression (RFR) as well as a hybrid method to combine them to downscale and improve 3-h accumulated radiation forecasts provided by Numerical Weather Prediction (NWP) systems for seven locations in Spain. We use either direct 3-h aggregated radiation forecasts or we build first global accumulated daily predictions and disaggregate them into 3-h values, with both approaches outperforming the base NWP forecasts. We also show how to disaggregate the 3-h forecasts into hourly values using interpolation based on clear sky (CS) theoretical and experimental radiation models, with the disaggregated forecasts again being better than the base NWP ones and where empirical CS interpolation yields the best results. Besides providing ample background on a problem that offers many opportunities to the Machine Learning (ML) community, our study shows that ML methods or, more generally, hybrid artificial intelligence systems are quite effective and, hence, relevant for solar radiation prediction.