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

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@Article{Mendonca-de-Paiva:2020:Energies,

• author = "Gabriel {Mendonca de Paiva} and Sergio {Pires Pimentel} and Bernardo {Pinheiro Alvarenga} and Enes {Goncalves Marra} and Marco Mussetta and Sonia Leva",
• title = "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: {MGGP} and {MLP} Neural Networks",
• journal = "Energies",
• year = "2020",
• volume = "13",
• number = "11",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1996-1073",
• URL = "https://www.mdpi.com/1996-1073/13/11/3005",
• DOI = "doi:10.3390/en13113005",
• abstract = "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 5.68percent for mean absolute error (MAE) and 3.41percent 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.",
• notes = "also known as \cite{en13113005}",

}

Genetic Programming entries for Gabriel Mendonca de Paiva Sergio Pires Pimentel Bernardo Pinheiro Alvarenga Enes Goncalves Marra Marco Mussetta Sonia Leva

Citations