Optimized Forecasting Approach for Scheduling Wind Generation Plants and Maximizing Renewable Energy Utilization-Reference-Cited by-同舟云学术

Optimized Forecasting Approach for Scheduling Wind Generation Plants and Maximizing Renewable Energy Utilization

Published:2024-04-23 Issue: Volume: Page:1-17
ISSN:1532-5008
Container-title:Electric Power Components and Systems
language:en
Short-container-title:Electric Power Components and Systems

Author:

Kumar Rahul¹,Sathiyasekar K.²,Nagabhooshanam N.³⁴⁵,Azam Mohammed⁶,Mukherjee Sarbojit⁷,Salunkhe Satish S.⁸,L Natrayan⁹,Anita S.¹⁰

Affiliation:

1. Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, India

2. Department of EEE, K S R Institute for Engineering and Technology, Tiruchengode, Namakkal, India

3. Department of Mechanical Engineering, Aditya Engineering College, Surampalem, India

4. Department of Mechanical Engineering, Institute of Engineering and Technology, GLA University Mathura, India

5. Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India

6. Electronic and Communication Engineering, ISL College of Engineering, International, Bandlaguda, Chandrayangutta, Hyderabad, Telangana, India

7. Department of Electrical Engineering, RCC Institute of Information Technology, Beliaghata, Kolkata, India

8. Computer Engineering Department, Terna Engineering College, Mumbai University, India

9. Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India

10. Department of Electrical and Electronics, R.M.K. Engineering College, Chennai, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15325008.2024.2337218

Reference43 articles.

1. Advanced forecasting models for wind power generation: a comparative study;Smith J.;IEEE Trans. Sustainable Energ.,2023

2. Optimal scheduling of renewable energy generation plants using genetic algorithms;Chen L.;Renewable Energ.,2023

3. Machine learning-based short-term wind power forecasting: a review;Lee S.;Energies,2022

4. Demand forecasting for smart grids: a comprehensive review;Wang C.;Electric Power Syst. Res.,2022

5. Enhancing wind power forecasting using weather data fusion techniques;Zhang W.;J. Renew. Sustain. Energ.,2022