Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models.

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 Me

SUBMITTER: Alcaniz A 

PROVIDER: S-EPMC9818063 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature