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Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms

Khosravi, A. ; Koury, R.N.N. ; Machado, L. ; Pabon, J.J.G.

Journal of cleaner production, 2018-03, Vol.176, p.63-75 [Periódico revisado por pares]

Elsevier Ltd

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  • Título:
    Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms
  • Autor: Khosravi, A. ; Koury, R.N.N. ; Machado, L. ; Pabon, J.J.G.
  • Assuntos: Adaptive neuro-fuzzy inference system ; Fuzzy logic ; Multilayer feed-forward ; Radial basis function ; Solar radiation ; Support vector regression
  • É parte de: Journal of cleaner production, 2018-03, Vol.176, p.63-75
  • Descrição: Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This study proposes machine learning algorithms to predict the hourly solar irradiance. Forecasting models were developed based two types of the input data. The first one uses local time, temperature, pressure, wind speed, and relative humidity as input variables of the models (N1); the second one is the time-series prediction of solar irradiance (N2) (forecasting models only use from past time-series solar radiation values to estimate the future values). For this purpose, multilayer feed-forward neural network (MLFFNN), radial basis function neural network (RBFNN), support vector regression (SVR), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) are developed. The results demonstrated that for the N1, SVR and MLFFNN models have the maximum performance to predict the solar irradiance with R = 0.9999 and 0.9795, respectively. For the N2, SVR, MLFFNN and ANFIS models have reported the correlation coefficient more than 0.95 for the testing dataset. •Two networks were developed in order to predict the solar irradiance.•The first network uses five inputs to predict the solar irradiance (N1).•The second network is the time-series prediction of solar radiation (N2).•MLFFNN, RBFNN, FIS and SVR models were developed for the N1.•MLFFNN, SVR, FIS and three types of ANFIS models were developed for the N2.
  • Editor: Elsevier Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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