Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Mendonca-de-Paiva:2020:Energies,
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author = "Gabriel {Mendonca de Paiva} and
Sergio {Pires Pimentel} and Bernardo {Pinheiro Alvarenga} and
Enes {Goncalves Marra} and Marco Mussetta and Sonia Leva",
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title = "Multiple Site Intraday Solar Irradiance Forecasting by
Machine Learning Algorithms: {MGGP} and {MLP} Neural
Networks",
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journal = "Energies",
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year = "2020",
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volume = "13",
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number = "11",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1996-1073",
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URL = "https://www.mdpi.com/1996-1073/13/11/3005",
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DOI = "doi:10.3390/en13113005",
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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.",
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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