Short-Term Forecasting Photovoltaic Solar Power for Home Energy Management Systems
نویسندگان
چکیده
Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which needed successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step forecasts used model-based predictive control for home management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) data selected by an approximate convex-hull algorithm, it shown that excellent results can be obtained. Two case studies are used: special house located the USA, and other typical residential situated south of Portugal. In latter case, one-step-ahead values unscaled root mean square error (RMSE), relative (MRE), normalized average (NMAE), absolute percentage (MAPE) R2 0.16, 1.27%, 1.22%, 8% 0.94 were obtained, respectively. These compare very favorably existing alternatives found literature.
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ژورنال
عنوان ژورنال: Inventions
سال: 2021
ISSN: ['2411-5134']
DOI: https://doi.org/10.3390/inventions6010012