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

Gabriel Mendonça de Paiva, Sergio Pires Pimentel, Bernardo Pinheiro Alvarenga, Enes Gonçalves Marra, Marco Mussetta and Sonia Leva
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Gabriel Mendonça de Paiva: School of Electrical, Mechanical, and Computer Engineering, Federal University of Goias (UFG), Goiania 74605-010, Brazil
Sergio Pires Pimentel: School of Electrical, Mechanical, and Computer Engineering, Federal University of Goias (UFG), Goiania 74605-010, Brazil
Bernardo Pinheiro Alvarenga: School of Electrical, Mechanical, and Computer Engineering, Federal University of Goias (UFG), Goiania 74605-010, Brazil
Enes Gonçalves Marra: School of Electrical, Mechanical, and Computer Engineering, Federal University of Goias (UFG), Goiania 74605-010, Brazil
Marco Mussetta: Department of Energy, Politecnico di Milano, 20156 Milano, Italy
Sonia Leva: Department of Energy, Politecnico di Milano, 20156 Milano, Italy

Energies, 2020, vol. 13, issue 11, 1-28

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 5.68% 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.

Keywords: solar irradiance forecasting; multigene genetic programming; multilayer perceptron; artificial neural networks; short-term forecasting; intraday forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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