Leveraging Deep Learning Architectures for Accurate Wind Speed and Power Prediction in Renewable Energy Systems DOI

V Alekhya,

R J Anandhi,

Alok Jain

и другие.

Опубликована: Сен. 18, 2024

Язык: Английский

Combined Multi-Component Composite Time Series Power Prediction Model for Distributed Energy Systems Based on Stl Data Decomposition DOI
Tianbo Yang, Shiying He, Xiaojiao Chen

и другие.

Опубликована: Янв. 1, 2024

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Язык: Английский

Процитировано

0
Microgrid Bidding Strategy with Uncertain Renewable Sources and Interruptible Loads in Energy and Spinning Reserve Markets DOI
Hossein Shahinzadeh

Опубликована: Март 6, 2024

Язык: Английский

Процитировано

0
Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach DOI Creative Commons
Dimitrios Michos, Francky Catthoor, Dimitris Foussekis

и другие.

Energies, Год журнала: 2024, Номер 17(21), С. 5493 - 5493

Опубликована: Ноя. 2, 2024

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for given wind turbine (WT) designed ultra-short-term (UST) energy forecasting over complex terrain. The core suggested modeling approach is Spatial Extrapolation model (WiSpEx). Measured vertical profile data are used as inlet stationary CFD simulations to reconstruct flow farm (WF). field reconstruction helps operators obtain speed available at hub height installed WTs, enabling estimation their production. WT power output calculated by accounting average time it takes adjust its in response changes speed. proposed evaluated with from two WTs (E40-500, NM 750/48). dataset this study contains ramp events speeds that range magnitude 3 m/s 18 m/s. results show can achieve Symmetric Mean Absolute Percentage Error (SMAPE) 8.44% E40-500 9.26% 750/48, even significant simplifications, while SMAPE persistence above 15.03% 16.12% 750/48. Each forecast requires less than minutes computational low-cost commercial platform. performance comparable state-of-the-art methods significantly faster time-dependent simulations. Such necessitate excessive resources, making them impractical online forecasting.

Язык: Английский

Процитировано

0
Machine Learning Algorithms for Predictive Maintenance in Hybrid Renewable Energy Microgrid Systems DOI Creative Commons
P.B. Edwin Prabhakar,

S. Rajarajeswari,

Sonali Antad

и другие.

E3S Web of Conferences, Год журнала: 2024, Номер 591, С. 05002 - 05002

Опубликована: Янв. 1, 2024

The rapid expansion of hybrid renewable energy microgrid systems presents new challenges in maintaining system reliability and performance. This paper explores the application machine learning algorithms for predictive maintenance such systems, focusing on early detection potential failures to optimize operational efficiency reduce downtime. By integrating real-time data from solar, wind, storage components, proposed models predict remaining useful life (RUL) critical components. results demonstrate significant improvements accuracy, offering a robust solution enhancing longevity microgrids.

Язык: Английский

Процитировано

0
Leveraging Deep Learning Architectures for Accurate Wind Speed and Power Prediction in Renewable Energy Systems DOI

V Alekhya,

R J Anandhi,

Alok Jain

и другие.

Опубликована: Сен. 18, 2024

Язык: Английский

Процитировано

0