Electrical grid
10 July 2025
Smart Grids: AI is revolutionizing energy distribution
Artificial intelligence (AI) is the great driver of change and transformation in the present and future of our society. This also applies to power grids and the energy sector. The work of research groups such as SIPBA (“Signal Processing and Biomedical Applications”) at the University of Granada, dedicated to the development of advanced signal processing and AI models for biomedicine, can serve as inspiration for other critical areas, such as energy and distribution networks.
By Enrique Herrera Viedma
Artificial intelligence (AI) is the great driver of change and transformation in the present and future of our society. Its ability to analyze large-scale data, detect complex patterns and make real-time, context-aware decisions makes it a fundamental technology for addressing new social, economic and environmental challenges. Although its most direct applications are in fields such as healthcare, transport or telecommunications, today more than ever—following the recent events resulting from the April 28, 2025 blackout—its application to the energy sector, and particularly to distribution networks, offers a novel field with enormous potential.
In this field, research groups such as SIPBA (“Signal Processing and Biomedical Applications”) at the University of Granada have developed over recent decades advanced signal processing and AI models which, conceived in the biomedical domain, can serve as inspiration for other critical areas such as energy. This type of methodological approach—often based on feature extraction, machine learning and intelligent modeling—is applicable to the processing of complex and multimodal data in energy distribution networks to perform grid monitoring tasks and fault detection and prediction.
AI to improve quality of life
The primary objective of any technological advance should be to improve people’s quality of life. In this regard, AI has already had a positive impact on many facets of everyday life: from computer-aided medical diagnosis (CAD) to intelligent navigation systems in aeronautics or home automation, known as domotics. In all these cases, what underlies is a combination of sensors (measurements), intelligent data processing (algorithms) and an optimized, context-aware system response (decisions).
In the energy sector, AI should help build more efficient, sustainable and user-centered systems. Distribution networks, traditionally passive, can become smart and resilient grids (“smart grids”), capable of dynamically managing demand, anticipating faults or overheating, providing higher-quality service to users, integrating renewable energy and enabling more efficient use of resources. All of this is of critical importance following the events of last April.
The distribution network: a critical and dynamic infrastructure
Energy distribution networks are an essential component of any electrical system. They transport electricity from substations to end users and must operate continuously, safely and efficiently. However, growing demand, the decentralization of generation (solar panels, batteries, electric vehicles) and the need to integrate renewable energy have increased their complexity. Unfortunately, the event last April reaffirms the importance of the distribution network, making it imperative to equip the system with analytical and management capabilities that take all possible factors into account—an essential requirement for the smart grids of the future.
To manage this new reality, static rules or deterministic models are no longer sufficient (as recent experience has shown); what is required is an “agnostic” system capable of learning, with extrapolation capabilities, able to adapt to abnormal situations and anticipate undesirable events—precisely what AI offers as its core characteristic.
Intelligent signal processing in energy: lessons learned from the biomedical field
The SIPBA research group at the University of Granada (UGR) has developed advanced models for the analysis of biomedical signals such as EEG, ECG or medical imaging, using filtering techniques, pattern detection, automatic classification and deep learning. Although these data originate from the human body, they share many characteristics with those generated in power grids (the new subject of analysis): they are complex, multichannel signals, affected by noise, time-varying and highly contextual.
Applying similar methodologies to power grids makes it possible to:
- Detect anomalies in the grid, such as voltage drops, overloads or non-technical losses, in the same way that areas of low activation are detected in neuroimaging (metabolism or perfusion).
- Predict electricity consumption patterns, using techniques similar to those applied in predicting clinical EEG episodes in epilepsy.
- Optimize energy flows, in a manner analogous to how signals are optimized in an ECG to obtain an accurate reading.
- Extract relevant features from the electrical environment that enable automatic or assisted decision-making, just as patterns defining Alzheimer’s or Parkinson’s disease are extracted.
- Establish new maintenance criteria for the elements that make up the distribution system (e.g. transformers) based on electrical measurements and thermography, in the same way normal patterns are modeled when searching for the etiology of various neurodegenerative diseases.
This interdisciplinary methodological transfer, from biomedicine to energy, is one of the most promising paths towards advancing robust, reliable and real-time intelligent systems.
Concrete AI applications in distribution networks
Artificial intelligence is already being applied in specific projects for the maintenance and monitoring of distribution networks. Specifically, it is used for:
- Predictive maintenance: just as an epileptic seizure can be anticipated through EEG analysis, it is also possible to predict failures in transformers or lines by analyzing historical data.
Ejemplo de mantenimiento predictivo para el mantenimiento de los elementos de la red de distribución (transformador de media a baja en la ciudad de Málaga, proyecto Pastora).
- Energy demand management: with predictive models based on neural networks or deep learning, it is possible to anticipate electricity consumption in a given area and adjust distribution accordingly.
- Loss or fraud detection: classification and pattern recognition techniques, similar to those used in medical diagnostics, make it possible to identify anomalous behavior in energy usage.
- Renewables integration: the prediction of photovoltaic or wind generation can be improved using models similar to those employed to predict physiological variables from multiple inputs.
- Rapid incident response: rule-based and learning-based decision algorithms make it possible to activate automatic protocols in the event of outages or disturbances in the grid.
Curvas de consumo real y previsto empleando imágenes de termografía e IA.
Social and environmental benefits of applying AI to the power grid
Applying signal-processing-inspired AI to the energy sector not only improves the system’s technical efficiency, but also delivers tangible benefits for society:
- Reduction in supply interruptions.
- Economic savings for businesses and households.
- Greater penetration of renewable energy.
- Lower environmental impact.
- Empowerment of end users, who can actively manage their consumption and production.
Algoritmos de IA para detección de aves en líneas eléctricas y protección del entorno natural.
Remaining challenges for the integration of AI in distribution networks
The integration of AI in distribution networks also poses significant challenges:
- Explainability of AI models: it is essential that decisions can be audited and understood, especially in critical sectors.
- Data quality: as in EEG analysis, if electrical sensor data are contaminated or incomplete, the model may fail.
- Interoperability: systems must be able to communicate with each other, share data and make decisions jointly.
- Ethics and privacy: especially when household consumption data are collected, ensuring information protection is essential.
Algoritmos de IA para la monitorización del estado de la línea y toma de decisiones desarrollado en el proyecto RESISTO.
AI applied to energy distribution networks has the potential to completely transform the electrical system, making it more efficient, sustainable, resilient and citizen-oriented. The experience of the SIPBA research group at the University of Granada in intelligent analysis of complex signals, automatic event detection and the design of machine learning models provides a solid methodological foundation to address these challenges.
Drawing inspiration from biomedical approaches to solve energy-related problems is a clear example of the power of interdisciplinarity and of how AI can ultimately improve people’s lives from different angles, making a smarter, more connected and sustainable future possible.
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