AI-Driven Approaches for Renewable Energy and Smart Grids: Observability, Flexibility and Management
A Special Issue of Energies (ISSN 1996-1073) belonging to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 25 March 2027 | Viewed by 51
Editors
Interests: developing computational intelligence and signal processing techniques; neural networks and deep learning methods for smart energy systems; edge-centric non-intrusive load monitoring algorithms; artificial intelligence; machine learning algorithms
Interests: digital signal processing (DSP) and artificial intelligence (AI) application to smart grids; electrical machines; smart energy systems; efficiency of next-generation energy networks; condition monitoring; predictive maintenance; non-intrusive load monitoring (NILM); behind-the-meter renewable power disaggregation; power signal analysis for anomaly detection
Interests: data science; machine learning; human–computer interaction; practical solutions for future energy systems and sustainable built environments; real-world deployment and evaluation of monitoring technologies and software systems; impact on energy and sustainability domains
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Special Issue Information
Dear Colleagues,
The rapid evolution of smart grids is reshaping modern energy infrastructures, transforming the interaction between renewable energy sources, conventional generation systems and dynamic consumer demand. In this scenario, AI-driven approaches are foundational for ensuring observability, operational flexibility and intelligent management of increasingly complex energy systems.
Advanced Metering Infrastructure (AMI), distributed sensors and edge-connected devices generate high-resolution data on load behaviour, local generation, storage dynamics and power quality. These data streams enable enhanced observability, but their real value emerges when AI models convert them into actionable intelligence for grid operation and planning.
Beyond monitoring, flexibility becomes a core objective of the new energy paradigm. The large-scale integration of distributed generation, renewable variability, prosumers, electric mobility and storage systems require continuous balancing between supply and demand across multiple time scales. AI supports this flexibility through short-term forecasting, adaptive demand response, dynamic load shaping, storage scheduling and coordinated control of distributed energy resources, improving both resilience and service continuity.
At the same time, management evolves from static, centralized control toward data-driven, distributed and predictive orchestration. AI-enabled management frameworks support real-time decision making, congestion mitigation, fault anticipation, asset optimization and market-aware energy dispatch. This enables grid operators and aggregators to reduce operational uncertainty, improve resource utilization, and align technical constraints with economic and sustainability goals.
In this perspective, observability, flexibility and management are tightly coupled dimensions of smart grid intelligence. Their integration through neural networks and computational intelligence techniques provides adaptive, robust and scalable solutions for next-generation power systems, accelerating the transition toward resilient, sustainable and renewable-centric energy infrastructures.
Potential topics include, but are not limited to:
- Behind-the-meter renewable power disaggregation;
- Distributed Energy Resource (DER) observability;
- Advanced monitoring for situational awareness, grid resilience and DER integration;
- Non-intrusive load monitoring;
- Interpretable, fair and explainable AI for Smart Energy Systems;
- Distributed and collaborative intelligence for smart grid monitoring and control;
- AI-driven energy management solutions (e.g., inclusive demand flexibility, e-mobility justice, sustainable agriculture);
- Smart charging for grid stability and load balancing;
- Generative AI for empowering power grid monitoring and stability;
- Forecasting of energy generation, demand and load profiles;
- Collaborative intelligence for energy monitoring;
- AI-driven multi-energy integrated systems;
- Smart charging for grid stability and load balancing;
- Power signal analysis for anomaly detection (e.g., disturbances, energy theft, power quality).
Dr. Giulia Tanoni
Dr. Emanuele Principi
Dr. Lucas Pereira
Guest Editors
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
- AI-driven smart grid observability
- multi-timescale demand–supply balancing
- AI-enhanced demand response
- smart energy flexibility
- AI-enabled power grid management
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