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- W2979648277 abstract "In this paper, the application of machine learning methods to predict the day ahead photovoltaic power generation in hourly intervals from the previous days, without using any exogenous data, have been studied. In order to select the relevant features, a random forest feature selection is used. This paper proposes a forecasting approach based on ensembles of artificial neural networks and support vector regression. The focus of this paper is on a single installed photovoltaic system, and in order to evaluate the performance of the proposed approaches, the measured data related to the photovoltaic installation on the roof of EnergyVille-1 is used. The results show that proposed approach can improve the accuracy of forecasting." @default.
- W2979648277 created "2019-10-18" @default.
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- W2979648277 date "2019-08-01" @default.
- W2979648277 modified "2023-09-24" @default.
- W2979648277 title "Ensemble Machine Learning Forecaster for Day Ahead PV System Generation" @default.
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- W2979648277 doi "https://doi.org/10.1109/sege.2019.8859918" @default.
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