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 originale | English |
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Pagine | 1152-1157 |
Numero di pagine | 6 |
Stato di pubblicazione | Published - 2017 |
All Science Journal Classification (ASJC) codes
- ???subjectarea.asjc.2100.2102???
- ???subjectarea.asjc.2100.2105???