RT Journal Article
T1 Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques
A1 López Cuesta, Miguel
A1 Aler, Ricardo
A1 Galván, Inés M.
A1 Pozo Vázquez, David
AB Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blendedmodels-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting
PB MDPI
SN 2072-4292
YR 2023
FD 2023-04-28
LK https://hdl.handle.net/10016/37284
UL https://hdl.handle.net/10016/37284
LA eng
NO This work was financed by the Junta de Andalucía, project PROMESOLAR (Programa Operativo FEDER Andalucía 2014–2020, ref. 1260136). The authors are supported by the Junta de Andalucía (Research group TEP-220). This publication is part of the I+D+i project PID2019-107455RB-C22, funded by MCIN/AEI/10.13039/501100011033. This work was also supported by the Comunidad de Madrid Excellence Program.
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RD 17 jul. 2024