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