Ultra-short-term wind speed prediction based on deep spatial-temporal residual network DOI Open Access
Xinhao Liang, Feihu Hu, X. Li

et al.

Journal of Renewable and Sustainable Energy, Journal Year: 2023, Volume and Issue: 15(4)

Published: July 1, 2023

To maintain power system stability, accurate wind speed prediction is essential. Taking into account the temporal and spatial characteristics of in an integrated manner can improve accuracy prediction. Considering complex nonlinear factors such as wake effects farms, a deep residual network valuable predicting with high degree accuracy. Wind data are typically time series that requires feature extraction attribute modeling, while maintaining signal integrity. In order to measure importance different attributes effectively aggregate features, we used parameter fusion matrix. We introduce spatial-temporal (DST-ResNet) for extracts characteristics, which forecast future multi-site farm particular region. this model, data's nearby property periodic separately modeled using network. The outputs two components dynamically aggregated matrix then fused additional meteorological features achieve Based on from National Renewable Energy Laboratory, our experiments show proposed DST-ResNet improves by 8.90%.

Language: Английский

A review of enhancing wind power with AI: applications, economic implications, and green innovations DOI Creative Commons
Yongkui Sun, Weixue Han

Digital Economy and Sustainable Development, Journal Year: 2025, Volume and Issue: 3(1)

Published: May 1, 2025

Abstract Wind energy, a renewable resource characterized by its inexhaustibility and absence of pollutants, has garnered significant attention in recent years. The optimization wind power generation for both economic environmental benefits emerged as solution to contemporary energy challenges. Artificial intelligence (AI), particularly machine learning (ML), enhances the efficiency sustainability systems. This study employs systematic literature review (SLR) methodology examine relevant literature. findings indicate that AI, predominantly represented ML hybrid AI models, contributes systems three primary domains: first, forecasting analysis variables, second turbines (WTs) performance through advanced maintenance management condition monitoring, finally farm layout optimization. Subsequently, we discussed how facilitates optimizes employment consumption structures, promotes green transformation enterprises, drives innovation industry variable turbine maintenance. application domain presents opportunities restructuring landscape. Efforts could be made accelerate AI-driven sector promote transformative reorganization industry.

Language: Английский

Citations

0
Wind resource assessment and optimization of wind farm layout at Kayathar, Tamil Nadu, India using WAsP DOI Creative Commons

Rajadurai Mani Jabez,

V. Kirubakaran

Deleted Journal, Journal Year: 2025, Volume and Issue: 7(6)

Published: May 21, 2025

Language: Английский

Citations

0
Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria DOI Creative Commons
A. M. Ismail,

J. M. Umar,

J. K. Sagir

et al.

Published: March 11, 2024

Climate change always had a massive effect on worldwide cities. which can only be decreased through considering renewable energy sources (wind energy, solar energy). However, the need to focus wind prediction will best solution world electricity petition. Wind power (WP) estimating techniques have been used for diverse literature studies many decades. The hardest way improve WP is its nature of differences that make it tough undertaking forecast. In line with outdated ways predicting speed (WS), employing machine learning methods (ML) has become an essential tool studying such problem. methodology this study focuses sanitizing efficient models precisely predict regimens. Two ML were employed “Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN)” WS estimation. experimental prediction. prophecy trained using 24-hour’ time-series data driven from Kano state Region, one biggest cities in Nigeria. Thus, investigating forecast performance was done terms coefficient determination (R²), linear correlation (R), Mean Square Error (MSE), Root square error (RMSE). Were. predicted result shows FFNN produces superior outcomes compared GPR. With R²= 1, R = MSE 6.62E-20, RMSE 2.57E-10

Language: Английский

Citations

3
Unlocking the potential: A review of artificial intelligence applications in wind energy DOI Creative Commons
Safa Dörterler, Seyfullah Arslan, Durmuş Özdemir

et al.

Expert Systems, Journal Year: 2024, Volume and Issue: 41(12)

Published: Aug. 27, 2024

Abstract This paper presents a comprehensive review of the most recent papers and research trends in fields wind energy artificial intelligence. Our study aims to guide future by identifying potential application areas intelligence machine learning techniques sector knowledge gaps this field. Artificial offer significant benefits advantages many sub‐areas, such as increasing efficiency facilities, estimating production, optimizing operation maintenance, providing security control, data analysis, management. focuses on studies indexed Web Science library between 2000 2023 using sub‐branches neural networks, other methods, mining, fuzzy logic, meta‐heuristics, statistical methods. In way, current methods literature are examined produce more efficient, sustainable, reliable energy, findings discussed for studies. evaluation is designed be helpful academics specialists interested acquiring broad perspective types uses seeking what subjects needed

Language: Английский

Citations

3
A novel approach to wind energy modeling in the context of climate change at Zaafrana region in Egypt DOI Creative Commons

Bassem Khaled Kamel,

Almoataz Y. Abdelaziz, Mahmoud A. Attia

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 4, 2025

Language: Английский

Citations

0
Wind speed prediction based on variational mode decomposition and advanced machine learning models in zaafarana, Egypt DOI Creative Commons

Ali Taha,

Nathalie Nazih, Peter Makeen

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 4, 2025

Language: Английский

Citations

0
Renewable Energy Role in Climate Stabilization and Water Consumption Minimization in Jordan DOI Open Access
Ayman Al‐Quraan, Hiba H. Darwish, Ahmad M. A. Malkawi

et al.

Processes, Journal Year: 2023, Volume and Issue: 11(8), P. 2369 - 2369

Published: Aug. 7, 2023

Climate change is one of the most essential phenomena studied by several researchers in last few decades. The main reason this phenomenon occurs greenhouse gases (GHG), chiefly CO2 emissions. About 30% created GHG emissions are achieved electricity generation. This article investigates role renewable energy projects Jordan, specifically wind and solar energy, mitigating climate water consumption reduction using RETScreen software. It was found that cumulative from 2017 to 2021 due use equal 6.9491 × 109 gallons. Finally, results show future dependence on Jordan meet growth demand year 2030 reduces expected increment temperature 1.047 °C year.

Language: Английский

Citations

6
A hybrid deep learning methodology for wind power forecasting based on attention DOI
Yıldırım Akbal, Kamil Demirberk Ünlü

International Journal of Green Energy, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 10

Published: Sept. 5, 2024

Language: Английский

Citations

2
A novel ultra short-term wind power prediction model based on double model coordination switching mechanism DOI
Mao Yang, Da Wang, Wei Zhang

et al.

Energy, Journal Year: 2023, Volume and Issue: 289, P. 130075 - 130075

Published: Dec. 19, 2023

Language: Английский

Citations

3
Optimizing Hybrid Photovoltaic/Battery/Diesel Microgrids in Distribution Networks Considering Uncertainty and Reliability DOI Open Access
Zulfiqar Ali Memon, Mohammad Amin Akbari

Sustainability, Journal Year: 2023, Volume and Issue: 15(18), P. 13499 - 13499

Published: Sept. 8, 2023

Due to the importance of allocation energy microgrids in power distribution networks, effect uncertainties their generation sources and inherent uncertainty network load on problem optimization performance should be evaluated. The optimal design a hybrid microgrid system consisting photovoltaic resources, battery storage, backup diesel generator are discussed this paper. objective is minimizing costs losses, resources generation, as resource, storage well shedding with determination components include its installation location 33-bus size PVs, batteries, Diesel generators. Additionally, radiation demand evaluated allocation. A Monte Carlo simulation used explore full range possibilities determine decision based variability inputs. For an accurate assessment system’s reliability, forced outage rate (FOR) analysis performed calculate potential losses that could affect operational probability system. cloud leopard (CLO) algorithm proposed optimize problem. effectiveness terms accuracy convergence speed verified compared other state-of-the-art methods. To further improve algorithm, reliability resource production investigated.

Language: Английский

Citations

1