Deep Learning Driven Renewable Energy Forecasting Using Distributed Cloud Computing and Large Scale Weather Data

Ashvini S. Kolate; Vikas M. Somvanshi

doi:10.70589/JRTCSE.2026.14.1.3

Authors

Ashvini S. Kolate MSc. Computer Science, PO Nahata College Bhusawal, Maharashtra 425201 Author
Vikas M. Somvanshi Lecturer in Department of Computer Engineering, SSVPS College Of Engineering, Dhule, Maharashtra 424005 Author

DOI:

https://doi.org/10.70589/JRTCSE.2026.14.1.3

Keywords:

Renewable Energy Forecasting, Deep Learning, Solar Power Prediction, Wind Power Prediction, Distributed Cloud Computing, Big Data Analytics, Transformer Models, LSTM, Smart Grid

Abstract

Renewable energy sources such as solar and wind are expanding rapidly; however, their output fluctuates with weather conditions. This variability makes accurate forecasting essential for power grid management and energy planning. Traditional forecasting methods often struggle to model complex weather data effectively. Recent advances in deep learning and cloud computing enable more accurate and scalable forecasting approaches. This paper reviews deep learning methods for renewable energy forecasting, with particular emphasis on systems that integrate distributed cloud computing and large-scale weather datasets. We organize the existing literature according to model type, infrastructure design, and data source. We then compare recent studies in terms of accuracy, scalability, and computational efficiency. Finally, we highlight key challenges and research gaps and propose future directions for developing smarter and more sustainable forecasting systems.

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