Challenges and Future Trends of Artificial Intelligence for Energy Management
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 10 November 2025 | Viewed by 4
Special Issue Editor
Special Issue Information
Dear Colleagues,
The global push towards decarbonization and sustainable development has put energy management at the heart of smart grids, industrial optimization, and renewable energy integration. In recent times, artificial intelligence (AI) has become a transforming tool in this space, offering smart solutions to tackle issues such as energy forecasting, usage optimization, load balancing, fault detection, demand management, and energy trade systems. AI helps better decision-making via predictive analytics, real-time data monitoring, and adaptive control, which all together enhance energy efficiency and system reliability across many sectors.
Still, despite notable progress, several challenges remain. These include the scalability of AI models in complex and large-scale energy networks, lack of clean data or data privacy issues, and the difficulties in real-time usage, as well as limited generalizability and clear interpretability of current algorithms. Moreover, combining AI with new trends such as digital twins, federated learning, edge computing, or even blockchain-based energy platforms brings both potential and technical barriers that deserve deeper exploration.
This Special Issue welcomes papers that push the limits of AI in energy management by tackling ongoing challenges and diving into fresh ideas, data examples, practical scenarios, and evaluation standards. The goal is to give a platform for researchers, engineers, and policymakers to reflect and share their outlooks on future trends and open issues in this fast-moving field.
Topics of interest include, but are not limited to, the following:
- AI-powered energy forecasting and usage pattern recognition;
- Reinforcement learning + deep learning techs for smart-grid optimization;
- Federated and privacy-aware AI approaches in distributed energy networks;
- Digital twins applied for predictive maintenance and simulation in energy;
- Edge AI for real-time control and IoT integration in smart environments;
- Explainable/interpretable AI tools to support energetic decision-making;
- Energy-aware scheduling and building control in data centers;
- Intelligent management of renewable resources (solar, wind, etc.);
- AI-based optimization for EV energy usage and V2G systems;
- Mixed AI models (physics-based and data-driven) for energy management;
- Anomaly detection and fault diagnosis in power infrastructure via AI;
- Optimizing energy trade and applications of blockchain in the energy sector;
- Real-world case studies on how AI is used in the Industry 4.0 energy area;
- Data availability, benchmarks, and standardization for AI-based energy management.
Prof. Dr. Wathiq Mansoor
Guest Editor
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence (AI)
- deep learning
- digital twins
- energy management
- smart grids
- energy forecasting
- distributed energy
- renewable resources
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