Wind turbine and PV power prediction using a deterministic data-driven model with variational mode decomposition preprocessing DOI
Saida El Bakali, Hamid Ouadi, Saad Gheouany

et al.

Transactions of the Institute of Measurement and Control, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 16, 2024

This study develops a method for accurately forecasting solar radiation (SR), wind speed (WS), and air temperature (AT) the coming 24 hours in order to predict energy production from photovoltaic (PV) panels turbines (WT) positive buildings. Input data are pre-processed through variational mode decomposition (VMD) broadband feature extraction, which is then decomposed into smooth modes. The application of Salp Swarm Algorithm (SSA) aims optimize VMD parameters enhance precision extraction. A thorough analysis performed identify essential input features. Residual pre-processing between variables their modes further enhances model performance. stacking algorithm (SA) used both residuals data. Performance evaluation using metrics such as root mean square error (RMSE), normalized (NRMSE), absolute (MAE), (NMAE) indicates reduction rates across measurement scales. For example, under adverse weather conditions, NRMSE NMAE PV power 2.50% 1.95%, respectively.

Language: Английский

Finite-Time control scheme for effective voltage and frequency regulation in networked microgrids DOI
Nima Khosravi

International Journal of Electrical Power & Energy Systems, Journal Year: 2025, Volume and Issue: 165, P. 110481 - 110481

Published: Jan. 22, 2025

Language: Английский

Citations

3
Probabilistic optimization of coordinated fuel Cell-CHP and renewable energy policy in microgrid integrated with hydrogen storage for optimizing system profitability DOI
Siwei Li, Congxiang Tian,

Hamid Faraji

et al.

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 102, P. 129 - 145

Published: Jan. 8, 2025

Language: Английский

Citations

1
Design and optimization of distributed energy management system based on edge computing and machine learning DOI Creative Commons
Nan Feng,

Conglin Ran

Energy Informatics, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 2, 2025

Language: Английский

Citations

0
Experimental and comparative study on optimal Active and Reactive Energy Management in microgrid: Moroccan VS Time of Use Tariff DOI
Saad Gheouany, Hamid Ouadi, Saida El Bakali

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 212, P. 115414 - 115414

Published: Jan. 30, 2025

Language: Английский

Citations

0
Challenges and prospectives of energy storage integration in renewable energy systems for net zero transition DOI
Subbarama Kousik Suraparaju,

Mahendran Samykano,

Vennapusa Jagadeeswara Reddy

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 125, P. 116923 - 116923

Published: May 12, 2025

Language: Английский

Citations

0
Wind turbine and PV power prediction using a deterministic data-driven model with variational mode decomposition preprocessing DOI
Saida El Bakali, Hamid Ouadi, Saad Gheouany

et al.

Transactions of the Institute of Measurement and Control, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 16, 2024

This study develops a method for accurately forecasting solar radiation (SR), wind speed (WS), and air temperature (AT) the coming 24 hours in order to predict energy production from photovoltaic (PV) panels turbines (WT) positive buildings. Input data are pre-processed through variational mode decomposition (VMD) broadband feature extraction, which is then decomposed into smooth modes. The application of Salp Swarm Algorithm (SSA) aims optimize VMD parameters enhance precision extraction. A thorough analysis performed identify essential input features. Residual pre-processing between variables their modes further enhances model performance. stacking algorithm (SA) used both residuals data. Performance evaluation using metrics such as root mean square error (RMSE), normalized (NRMSE), absolute (MAE), (NMAE) indicates reduction rates across measurement scales. For example, under adverse weather conditions, NRMSE NMAE PV power 2.50% 1.95%, respectively.

Language: Английский

Citations

1