Print Email Facebook Twitter Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models Title Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models Author Alcañiz Moya, A. (TU Delft Photovoltaic Materials and Devices) Lindfors, Anders V. (Finnish Meteorological Institute (FMI)) Zeman, M. (TU Delft Electrical Sustainable Energy) Ziar, H. (TU Delft Photovoltaic Materials and Devices) Isabella, O. (TU Delft Photovoltaic Materials and Devices) Department Electrical Sustainable Energy Date 2022 Abstract Machine learning is arising as a major solution for the photovoltaic (PV) power prediction. Despite the abundant literature, the effect of climate on yield predictions using machine learning is unknown. This work aims to find climatic trends by predicting the power of 48 PV systems around the world, equally divided into four climates. An extensive data gathering process is performed and open-data sources are prioritized. A website www.tudelft.nl/open-source-pv-power-databases has been created with all found open data sources for future research. Five machine learning algorithms and a baseline one have been trained for each PV system. Results show that the performance ranking of the algorithms is independent of climate. Systems in dry climates depict on average the lowest Normalized Root Mean Squared Error (NRMSE) of 47.6 %, while those in tropical present the highest of 60.2 %. In mild and continental climates the NRMSE is 51.6 % and 54.5 %, respectively. When using a model trained in one climate to predict the power of a system located in another climate, on average systems located in cold climates show a lower generalization error, with an additional NRMSE as low as 5.6 % depending on the climate of the test set. Robustness evaluations were also conducted that increase the validity of the results. Subject climateforecastingKöppen-Geigermachine learningphotovoltaics To reference this document use: http://resolver.tudelft.nl/uuid:ff23bdf0-e489-43cf-a734-862d514985f5 DOI https://doi.org/10.1002/gch2.202200166 Source Global Challenges, 7 (2023) (1) Part of collection Institutional Repository Document type journal article Rights © 2022 A. Alcañiz Moya, Anders V. Lindfors, M. Zeman, H. Ziar, O. Isabella Files PDF Global_Challenges_2022_Al ... Models.pdf 1.67 MB Close viewer /islandora/object/uuid:ff23bdf0-e489-43cf-a734-862d514985f5/datastream/OBJ/view