Takagi-sugeno fuzzy systems applied to voltage prediction of photovoltaic plants

High penetration level of intermittent and variable renewable electricity generation introduces signicant challenges to energy management of modern smart grids. Solar photo-voltaics and wind energy have uncertain and non-dispatchable output which leads to concerns regarding the technical and economic feasibility of a reliable integration of large amounts of variable generation into electric grids. In this scenario, accurate forecasting of renewable generation outputs is of paramount importance to secure operation of smart grids. In this paper, we present a study on the use of fuzzy neural networks and their application to the prediction of solar photovoltaic outputs. The new learning strategy is suited to any fuzzy inference model. The comparison with respect to well-known neural and fuzzy neural models will prove that our approach is able to follow the behavior of the underlying unknown process with a good prediction of the observed time series.

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