Renewable Energy Forecasting Using Deep Learning Models for Sustainable Power Systems

Regina Barzilay

Renewable Energy Forecasting Using Deep Learning Models for Sustainable Power Systems

Author(s)	Regina Barzilay
Country	United States
Abstract	The increasing integration of renewable energy resources into modern power systems has transformed the global energy landscape while introducing significant operational challenges due to the intermittent and uncertain nature of renewable generation. Accurate forecasting of renewable energy production is essential for maintaining grid stability, improving energy management, reducing operational costs, and supporting sustainable development. Deep Learning (DL) has emerged as a powerful computational approach capable of modelling complex nonlinear relationships within large-scale meteorological, environmental, and historical energy datasets. Advanced deep learning architectures such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Transformers, and hybrid deep learning models have demonstrated remarkable performance in forecasting solar, wind, hydropower, and hybrid renewable energy generation. This study investigates renewable energy forecasting using deep learning models through a qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, international energy reports, government publications, and multidisciplinary case studies. The study examines forecasting techniques, feature engineering, weather prediction, smart grids, Internet of Things (IoT), cloud computing, edge intelligence, digital twins, explainable artificial intelligence (XAI), and intelligent energy management systems. Furthermore, it evaluates the contribution of deep learning to sustainable power systems, grid reliability, demand-response management, energy trading, and carbon emission reduction while identifying implementation challenges and future research opportunities. The findings indicate that deep learning significantly improves forecasting accuracy, renewable energy integration, grid resilience, operational efficiency, and energy sustainability. Hybrid AI models integrating meteorological forecasting, IoT-enabled sensing, cloud-edge computing, and explainable AI further strengthen predictive performance while enhancing transparency and decision support. However, challenges including data quality, computational complexity, model interpretability, cybersecurity, scalability, and uncertainty management continue to influence successful implementation. The study concludes that deep learning provides a comprehensive multidisciplinary framework for renewable energy forecasting and intelligent power system management. Collaboration among energy engineers, AI researchers, policymakers, utility operators, and environmental scientists is essential to establish reliable, sustainable, and resilient renewable energy ecosystems.
Keywords	Renewable Energy Forecasting, Deep Learning, Artificial Intelligence, Sustainable Power Systems, Smart Grid, Solar Energy, Wind Energy, LSTM, Energy Management.
Field	Engineering
Published In	Volume 1, Issue 2, March-April 2019
Published On	2019-04-02

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American Journal of Advanced Multidisciplinary Research and Innovation

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Renewable Energy Forecasting Using Deep Learning Models for Sustainable Power Systems

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