TY - GEN
T1 - Exploring the Limits of Machine Learning in the Prediction of Solar Radiation
AU - Scabbia, Giovanni
AU - Sanfilippo, Antonio
AU - Perez-Astudillo, Daniel
AU - Bachour, Dunia
AU - Fountoukis, Christos
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Predicting solar radiation at diverse time horizons is crucial for optimizing solar energy integration, ensuring grid stability, and regulating energy markets. Two main levels of time granularity are usually recognized as requiring different treatment: solar nowcasting for predictions up to 6 h, and solar forecasting for predictions beyond 6 h. Solar nowcasting typically relies on machine learning methods, while Numerical Weather Prediction (NWP) models are considered better suited for solar forecasting. The goal of this study was to explore the limits of machine learning in solar forecasting. Our results show that machine learning methods can be profitably used for predicting solar radiation beyond 6 h, with comparable performances to NWP models for day-ahead solar forecasting.
AB - Predicting solar radiation at diverse time horizons is crucial for optimizing solar energy integration, ensuring grid stability, and regulating energy markets. Two main levels of time granularity are usually recognized as requiring different treatment: solar nowcasting for predictions up to 6 h, and solar forecasting for predictions beyond 6 h. Solar nowcasting typically relies on machine learning methods, while Numerical Weather Prediction (NWP) models are considered better suited for solar forecasting. The goal of this study was to explore the limits of machine learning in solar forecasting. Our results show that machine learning methods can be profitably used for predicting solar radiation beyond 6 h, with comparable performances to NWP models for day-ahead solar forecasting.
KW - Autoregressive modeling
KW - Differencing
KW - Machine learning
KW - Solar radiation forecasting
UR - http://www.scopus.com/inward/record.url?scp=85129826505&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-76081-6_46
DO - 10.1007/978-3-030-76081-6_46
M3 - Conference contribution
AN - SCOPUS:85129826505
SN - 9783030760809
T3 - Advances in Science, Technology and Innovation
SP - 381
EP - 384
BT - Sustainable Energy-Water-Environment Nexus in Deserts - Proceeding of the 1st International Conference on Sustainable Energy-Water-Environment Nexus in Desert Climates
A2 - Heggy, Essam
A2 - Bermudez, Veronica
A2 - Vermeersch, Marc
PB - Springer Nature
T2 - 1st International Conference on Sustainable Energy-Water-Environment Nexus in Desert Climates, ICSEWEN 2019
Y2 - 2 December 2019 through 5 December 2019
ER -