Solar photovoltaic plants power output forecasting using machine learning techniques can be of a great advantage to energy producers when they are implemented with day-ahead energy market data. In this work a model was developed using a supervised learning algorithm of multilayer perceptron feedforward artificial neural network to predict the next twenty-four hours (day-ahead) power of a solar facility using fetched weather forecast of the following day. Each set of tested network configuration was trained by the historical power output of the plant as a target. For each configuration, one hundred networks ensembles was averaged to give the ability to generalize a better forecast. The trained ensembles performances were analyzed using statistical indicators. The best-performing model ensembles were eventually used to predict power from the automatically fetched weather data.

Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles

DOLARA, ALBERTO;MAGISTRATI, GIULIA;MUSSETTA, MARCO;OGLIARI, EMANUELE GIOVANNI CARLO;
2016-01-01

Abstract

Solar photovoltaic plants power output forecasting using machine learning techniques can be of a great advantage to energy producers when they are implemented with day-ahead energy market data. In this work a model was developed using a supervised learning algorithm of multilayer perceptron feedforward artificial neural network to predict the next twenty-four hours (day-ahead) power of a solar facility using fetched weather forecast of the following day. Each set of tested network configuration was trained by the historical power output of the plant as a target. For each configuration, one hundred networks ensembles was averaged to give the ability to generalize a better forecast. The trained ensembles performances were analyzed using statistical indicators. The best-performing model ensembles were eventually used to predict power from the automatically fetched weather data.
2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016
9781509033881
Component; Formatting; Insert (key words); Style; Styling; Energy Engineering and Power Technology; Renewable Energy, Sustainability and the Environment
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1031317
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