Short‐Term Ensemble Insolation Forecasting Method Using Parallelized NE

ABSTRACT In this paper, the authors propose an ensemble forecasting method using multiple individuals for short‐time‐ahead (1 h ahead) insolation forecasting by using neuroevolution (NE), in which a genetic algorithm is applied to the learning algorithm of a neural network for insolation. Although t...

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Bibliographic Details
Published inElectrical engineering in Japan Vol. 218; no. 1
Main Authors Kawasaki, Shoji, Ishibe, Koshi
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc 01.03.2025
Subjects
ensemble forecasting
Forecasting
genetic algorithm
Genetic algorithms
Insolation
insolation forecasting
Machine learning
Neural networks
neuroevolution
parallel processing
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Summary:ABSTRACT In this paper, the authors propose an ensemble forecasting method using multiple individuals for short‐time‐ahead (1 h ahead) insolation forecasting by using neuroevolution (NE), in which a genetic algorithm is applied to the learning algorithm of a neural network for insolation. Although the method improves the accuracy compared to a single forecast, NE has a problem that the training time is long. In order to solve this problem, the authors propose a parallelization method of GPU processing for short‐time‐ahead forecasting and try to solve the problem by parallelizing the GPU.
Bibliography:IEEJ Transactions on Power and Energy
10.1541/ieejpes.144.608
(Denki Gakkai Ronbunshi B).
Translated from Volume 144 Number 12, pages 608–615, DOI
of
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ISSN:0424-7760
1520-6416
DOI:10.1002/eej.23487