Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production

S. Ferlito, G. Adinolfi and G. Graditi

Applied Energy, 2017, vol. 205, issue C, 116-129

Abstract: Actual technology improvements are contributing to DC and AC micro grid diffusion characterized by renewables photovoltaic and storage systems. Photovoltaic technologies have the advantage of a capillary distribution, but they are characterized by an intrinsic variable behavior due to continuously changing weather conditions. This drawback can be overcome by an appropriate temporal and energetic match among photovoltaic generation and storage capacity, so increasing micro grids reliability and efficiency levels. An accurate forecast of photovoltaic production can contribute to smooth photovoltaic systems intermittency problems so supporting generation and storage balance.

Keywords: Photovoltaic generation; Machine learning; Performance evaluation; Prediction error; Training dataset optimization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (21)

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DOI: 10.1016/j.apenergy.2017.07.124

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