Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 9, Issue 2, Pages 831-842Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2017.2762435
Keywords
PV plants; machine learning algorithms; power generation forecasts
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The ability to accurately forecast power generation from renewable sources is nowadays recognized as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper, we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.
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