Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach

International Journal of Electrical Power & Energy Systems, Journal Year: 2022, Volume and Issue: 143, P. 108504 - 108504

Published: Aug. 2, 2022

Language: Английский

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Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach

Energy, Journal Year: 2023, Volume and Issue: 283, P. 129005 - 129005

Published: Sept. 9, 2023

Language: Английский

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A multi-stage predicting methodology based on data decomposition and error correction for ultra-short-term wind energy prediction

Journal of Cleaner Production, Journal Year: 2021, Volume and Issue: 292, P. 125981 - 125981

Published: Jan. 19, 2021

Language: Английский

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A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms

Applied Energy, Journal Year: 2021, Volume and Issue: 305, P. 117815 - 117815

Published: Sept. 15, 2021

Language: Английский

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Short term wind energy prediction model based on data decomposition and optimized LSSVM

Sustainable Energy Technologies and Assessments, Journal Year: 2022, Volume and Issue: 52, P. 102025 - 102025

Published: Feb. 10, 2022

Language: Английский

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A short-term wind speed interval prediction method based on WRF simulation and multivariate line regression for deep learning algorithms

Energy Conversion and Management, Journal Year: 2022, Volume and Issue: 258, P. 115540 - 115540

Published: March 28, 2022

Language: Английский

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Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review

Energies, Journal Year: 2023, Volume and Issue: 16(3), P. 1530 - 1530

Published: Feb. 3, 2023

As grid-connected wind farms become more common in the modern power system, question of how to maximize generation while limiting downtime has been a issue for researchers around world. Due complexity turbine systems and difficulty predict varying speeds, artificial intelligence (AI) machine learning (ML) algorithms have key components when developing controllers control schemes. Although, recent years, several review papers on these topics published, there are no comprehensive that pertain both AI ML available literature, especially with respect most recently published techniques. To overcome drawbacks existing an in-depth overview is presented this paper. This paper analyzes following reviews: (i) why optimizing farm important; (ii) challenges associated designing efficient scheme farms; (iii) breakdown different types used schemes; (iv) speed prediction; (v) (vi) mechanical component monitoring fault detection; (vii) electrical prevention detection. will offer engineers energy field application methodology offshore onshore so robust schemes can be designed future controllers.

Language: Английский

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A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation

Renewable Energy, Journal Year: 2024, Volume and Issue: 226, P. 120360 - 120360

Published: March 18, 2024

Language: Английский

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The Role of Utilizing Artificial Intelligence and Renewable Energy in Reaching Sustainable Development Goals

Renewable Energy, Journal Year: 2024, Volume and Issue: 235, P. 121311 - 121311

Published: Sept. 7, 2024

Language: Английский

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Large-scale optimal integration of wind and solar photovoltaic power in water-energy systems on islands

Energy Conversion and Management, Journal Year: 2021, Volume and Issue: 235, P. 113982 - 113982

Published: March 13, 2021

Language: Английский

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