Wind power forecasting system with data enhancement and algorithm improvement

Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 196, С. 114349 - 114349

Опубликована: Март 1, 2024

Язык: Английский

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A hybrid wind speed forecasting model with two-stage data processing based on adaptive neuro-fuzzy inference systems and deep learning algorithms

Earth Science Informatics, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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A new paradigm based on Wasserstein Generative Adversarial Network and time-series graph for integrated energy system forecasting

Energy Conversion and Management, Год журнала: 2025, Номер 326, С. 119484 - 119484

Опубликована: Янв. 13, 2025

Язык: Английский

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Ultra-short-term wind power forecasting jointly driven by anomaly detection, clustering and graph convolutional recurrent neural networks

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103137 - 103137

Опубликована: Янв. 25, 2025

Язык: Английский

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A combined interval prediction system based on fuzzy strategy and neural network for wind speed

Applied Soft Computing, Год журнала: 2024, Номер 155, С. 111408 - 111408

Опубликована: Фев. 23, 2024

Язык: Английский

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Multivariate rolling decomposition hybrid learning paradigm for power load forecasting

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 212, С. 115375 - 115375

Опубликована: Янв. 23, 2025

Язык: Английский

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A hybrid annual runoff prediction model using echo state network and gated recurrent unit based on sand cat swarm optimization with Markov chain error correction method

Journal of Hydroinformatics, Год журнала: 2024, Номер 26(6), С. 1425 - 1453

Опубликована: Май 16, 2024

ABSTRACT This study proposes a hybrid model based on the combination of Sand Cat Swarm Optimization (SCSO), Echo State Network (ESN), Gated Recurrent Unit (GRU), Least Squares Method (LSM), and Markov Chain (MC) to improve accuracy annual runoff prediction. Firstly, conduct correlation analysis multi-factor data related determine input model. Secondly, SCSO algorithm is used optimize parameters ESN GRU models, SCSO-ESN SCSO-GRU models are established. Next, prediction results these two coupled using LSM obtain preliminary SCSO-ESN-GRU Finally, initial corrected for errors MC get final results. Choose Changshui Station Lanxi experiments, evaluate predictive performance through five evaluation indicators. The show that combined by achieved optimal at both experimental stations. emphasizes chain correction can significantly prediction, providing reliable basis predicting in complex watersheds.

Язык: Английский

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A short-term power load forecasting system based on data decomposition, deep learning and weighted linear error correction with feedback mechanism

Applied Soft Computing, Год журнала: 2024, Номер 162, С. 111863 - 111863

Опубликована: Июнь 15, 2024

Язык: Английский

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Interpretable multi-graph convolution network integrating spatial-temporal attention and dynamic combination for wind power forecasting

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124766 - 124766

Опубликована: Дек. 1, 2024

Язык: Английский

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A point-interval wind speed forecasting system based on fuzzy theory and neural networks architecture searching strategy

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 132, С. 107906 - 107906

Опубликована: Янв. 28, 2024

Язык: Английский

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