Extreme Learning Machines for Solar Photovoltaic Power Predictions
AbstractThe unpredictability of intermittent renewable energy (RE) sources (solar and wind) constitutes reliability challenges for utilities whose goal is to match electricity supply to consumer demands across centralized grid networks. Thus, balancing the variable and increasing power inputs from plants with intermittent energy sources becomes a fundamental issue for transmission system operators. As a result, forecasting techniques have obtained paramount importance. This work aims at exploiting the simplicity, fast computational and good generalization capability of Extreme Learning Machines (ELMs) in providing accurate 24 h-ahead solar photovoltaic (PV) power production predictions. The ELM architecture is firstly optimized, e.g., in terms of number of hidden neurons, and number of historical solar radiations and ambient temperatures (embedding dimension) required for training the ELM model, then it is used online to predict the solar PV power productions. The investigated ELM model is applied to a real case study of 264 kWp solar PV system installed on the roof of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. Results showed the capability of the ELM model in providing predictions that are slightly more accurate with negligible computational efforts compared to a Back Propagation Artificial Neural Network (BP-ANN) model, which is currently adopted by the PV system owners for the prediction task. View Full-Text
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Al-Dahidi, S.; Ayadi, O.; Adeeb, J.; Alrbai, M.; Qawasmeh, B.R. Extreme Learning Machines for Solar Photovoltaic Power Predictions. Energies 2018, 11, 2725.
Al-Dahidi S, Ayadi O, Adeeb J, Alrbai M, Qawasmeh BR. Extreme Learning Machines for Solar Photovoltaic Power Predictions. Energies. 2018; 11(10):2725.Chicago/Turabian Style
Al-Dahidi, Sameer; Ayadi, Osama; Adeeb, Jehad; Alrbai, Mohammad; Qawasmeh, Bashar R. 2018. "Extreme Learning Machines for Solar Photovoltaic Power Predictions." Energies 11, no. 10: 2725.
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