کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406082 678059 2016 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hybrid machine learning forecasting of solar radiation values
ترجمه فارسی عنوان
پیش بینی ماشینی هیبریدی در مورد ارزش تابش خورشیدی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 176, 2 February 2016, Pages 48–59
نویسندگان
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