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

Fabio Viola, Dolara, Ogliari, Magistrati, Omar, Mussetta

Risultato della ricerca: Other

17 Citazioni (Scopus)

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
Lingua originaleEnglish
Pagine1152-1157
Numero di pagine6
Stato di pubblicazionePublished - 2017

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All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

Cita questo

Viola, F., Dolara, Ogliari, Magistrati, Omar, & Mussetta (2017). Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles. 1152-1157.