Hybrid Forecasting Model Integrating RNN-LSTM for Renewable Energy Production

Abstract

Renewable energy production forecasting is vital for effective grid management and energy trading. Traditional forecasting methods often fall short in capturing the complex and dynamic nature of renewable energy data. In this research, we propose a novel hybrid forecasting model that seamlessly integrates traditional electrical components with Recurrent Neural Networks using Long Short-Term Memory. Our model surpasses existing methods in forecasting accuracy, adaptability to changing conditions, and robustness to data quality. It provides valuable insights into long-term energy trends while reducing external dependencies. The hybrid model’s success extends beyond renewable energy forecasting, offering a template for addressing forecasting challenges in various domains. Our findings and contributions advance the field, promising a future where accurate and adaptable renewable energy forecasts drive sustainability and innovation in the energy sector and beyond.

Keywords:

• renewable energy forecasting
• hybrid forecasting models
• RNN-LSTM
• renewable energy production
• energy grid management
• data quality robustness
• adaptability
• energy sector innovation

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Sreekumar Narayanan

Sreekumar Narayanan is an Assistant Professor in the Faculty of Engineering and Technology at Botho University in Gaborone, Botswana. He has completed PhD in Computer Science from Bharathiar University, Coimbatore, India. He has more than 20 years of expertise in both academic and research teaching. He has numerous research articles that have been published in reputable publications and conferences. His areas of interest in research include image processing, big data analytics, and AI&ML. Email Id: [email protected].

Rajiv Kumar

Rajiv Kumar is working as an Associate Professor in Bennett University, Greater Noida, India. He has completed PhD in Computer Science from Bharathiar University, Coimbatore, India. He received his M.Tech degree in Computer science and Engineering from Guru Jambheshwar University of Science and Technology, Hisar (Haryana). He has published more than 30 research papers in various journals and conference including Springer, Elsevier etc. He has experience of 17 years in academics, and he has worked in various organizations. He is member of various professional bodies like IEEE, ACM, IEI, IETE, ISTE, CSI etc. His research interest includes data mining, pattern mining, image processing, and machine learning. Email Id: [email protected].

Sudhir Ramadass

Sudhir Ramadass Senior Consultant - Data Analytics, Sterck Systems Private Limited, Chennai, India. He has varied experience in the areas of image processing, big data analytics, business analytics and AI/ML. His 6+ years of IT industry has been widely on Data analytics. He has many research papers published in international journals as part of his 15+ years of academic and research experience. Email Id: [email protected].

Jayaraj Ramasamy

Jayaraj Ramasamy works as a Fellow at the Faculty of Engineering and Technology, Botho University (BU), Botswana, Africa. He holds PhD in Computer Science & Engineering from Azteca University, Mexico and Masters of Engineering in Multimedia Technology from Anna University, India. He has more than 10 years of teaching experience in Asia and Africa. He is certified as Android Certified Application Developer. He has published a number of research papers in reputed journals and holds an Australian and Indian patent. His Research interest is focused on the topics of Machine Learning, Computer Vision and Deep learning. Email Id: [email protected].