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Open AccessArticle

Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks

1
School of Electrical, Mechanical, and Computer Engineering, Federal University of Goias (UFG), Goiania 74605-010, Brazil
2
Department of Energy, Politecnico di Milano, 20156 Milano, Italy
*
Authors to whom correspondence should be addressed.
Energies 2020, 13(11), 3005; https://doi.org/10.3390/en13113005
Received: 6 April 2020 / Revised: 7 May 2020 / Accepted: 29 May 2020 / Published: 11 June 2020
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting)
The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort. View Full-Text
Keywords: solar irradiance forecasting; multigene genetic programming; multilayer perceptron; artificial neural networks; short-term forecasting; intraday forecasting solar irradiance forecasting; multigene genetic programming; multilayer perceptron; artificial neural networks; short-term forecasting; intraday forecasting
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MDPI and ACS Style

Mendonça de Paiva, G.; Pires Pimentel, S.; Pinheiro Alvarenga, B.; Gonçalves Marra, E.; Mussetta, M.; Leva, S. Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks. Energies 2020, 13, 3005. https://doi.org/10.3390/en13113005

AMA Style

Mendonça de Paiva G, Pires Pimentel S, Pinheiro Alvarenga B, Gonçalves Marra E, Mussetta M, Leva S. Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks. Energies. 2020; 13(11):3005. https://doi.org/10.3390/en13113005

Chicago/Turabian Style

Mendonça de Paiva, Gabriel; Pires Pimentel, Sergio; Pinheiro Alvarenga, Bernardo; Gonçalves Marra, Enes; Mussetta, Marco; Leva, Sonia. 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks" Energies 13, no. 11: 3005. https://doi.org/10.3390/en13113005

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