Deep Learning and Advanced Machine Learning for Energy Forecasting
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 March 2026 | Viewed by 21
Special Issue Editor
Special Issue Information
Dear Colleagues,
As the world pivots towards decarbonization and sustainability, the ability to precisely forecast energy consumption and carbon emissions is fundamental for grid stability, effective policy-making, and strategic corporate environmental management. This Special Issue will sit at the intersection of energy systems, data science, and artificial intelligence to address the urgent need for more accurate and reliable energy predictions.
This issue will highlight the paradigm shift from classical forecasting models to more sophisticated, data-driven techniques. We will feature research that leverages state-of-the-art machine learning and deep neural networks for granular energy consumption analysis and real-time carbon emission prediction. Furthermore, this issue will confront the practical challenges inherent in real-world energy data, presenting innovative solutions for handling incomplete datasets to ensure the development of robust predictive systems. In recognition of the growing complexity and data sensitivity in modern energy networks, we will also explore pioneering frameworks, including distributed and privacy-preserving approaches, for secure and efficient energy scheduling. This Special Issue will provide a timely snapshot of the cutting-edge research driving the future of intelligent and sustainable energy management.
I look forward to receiving your contributions.
Dr. Xin Zhou
Guest Editor
Manuscript Submission Information
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Keywords
- energy forecasting
- deep learning
- carbon emission prediction
- smart grids
- sustainable energy
- time-series analysis
- energy management
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