Experts from universities, companies and technology centers present solutions based on AI, IoT and digital twins to move towards a smart, sustainable and connected energy system
València, March 27, 2025 – The Fundación ValgrAI and the Clúster de Energía de la Comunidad Valenciana organized the event “Inteligencia Artificial en el Sector Energético: Estado Actual y Oportunidades” where new avenues for innovation in the sector were presented.
The event was opened by Ana Cidad, Managing Director of ValgrAI; Enrique Bayonne, Managing Director of the Clúster de Energía de la CV; and Rafael Sebastián, Director General of Science and Research, who highlighted the Generalitat’s support for technology transfer to companies.
Vicent Botti, General Director of ValgrAI and Director of the Instituto VRAIN, delivered the talk “Smart Energy Management in Smart Cities,” where he addressed how cities can optimize their energy systems through the integration of advanced technologies such as artificial intelligence, the Internet of Things (IoT), 5G networks, and blockchain.
During his presentation, Botti focused in particular on the transformative role of artificial intelligence in the urban energy field. He explained how predictive models based on machine learning can accurately anticipate energy demand, balance supply in real time, and significantly reduce the risk of failures in the electricity grid. He also demonstrated how AI enables predictive maintenance of critical infrastructure, optimization of electric vehicle fleets, personalized energy consumption in smart buildings, and automation of distributed microgrids. These insights were supported by real-world examples such as the use of AI in Google’s data centers, the urban energy network in Copenhagen, or the Tesla Autobidder platform.
Antoni Mestre and Manuela Albert from VRAIN addressed “Sustainability in Business Processes and AI,” showing how the integration of IoT devices into processes can act as a key catalyst for moving toward more sustainable organizational models. Through this technological integration, processes can capture real-time data from the environment, perform tasks autonomously or with assistance, and make more accurate, context-aware decisions — directly impacting operational efficiency and reducing environmental impact. Mestre, who gave the presentation, affirmed that IoT not only transforms operational methods but also opens new possibilities to align business activities with the Sustainable Development Goals (SDGs), positively influencing economic, social, technical, environmental, and even human dimensions.
This model helps identify tangible benefits such as improved energy efficiency, reduced errors, and increased traceability, as well as potential adverse effects such as implementation costs, technical management of devices, or privacy implications for users. The growing role of AI was also highlighted as a strategic tool to analyze historical and real-time data, detect consumption patterns, and suggest improvements aligned with each company’s sustainability objectives.
Paula Bastida Molina, professor and researcher at the Instituto de Ingeniería Energética and Director of the Cátedra de Transición Energética Urbana at the Universitat Politècnica de València, presented an innovative project applying artificial intelligence to the large-scale identification of urban rooftops suitable for installing solar photovoltaic systems and green roofs. This initiative addresses one of the major challenges of the energy transition: leveraging the untapped potential of the built environment to generate clean energy and mitigate the effects of climate change. Using advanced computer vision algorithms and deep segmentation models such as the Segment Anything Model (SAM), the research team has developed a methodology capable of analyzing large volumes of geospatial data (orthophotos, LIDAR files, and vector layers) to detect obstacle-free areas with sufficient solar irradiation, thereby optimizing the design and configuration of solar installations in urban environments.
The project also includes the analysis and mapping of rooftops suitable for green roof implementation — a solution that contributes to CO₂ capture, improved thermal insulation, and urban regeneration. The proposal is part of the DESVAB project, which combines AI, public data, and energy planning tools to support decision-making at the urban scale.
Emilio Soria Olivas, professor at the Universitat de València, Director of the Intelligent Data Analysis Laboratory (IDAL) and researcher at ValgrAI, presented the talk “AI and Energy: Transforming Generation, Management and Sustainability of the Future Energy System,” where he emphasized how AI is becoming a strategic pillar to tackle the main challenges in the energy sector. Through the development of predictive models, optimization systems, and big data-based tools, his team works on solutions that enable forecasting renewable energy production (solar, wind, or biomass), predicting energy demand with high accuracy across different geographic areas, and making smart real-time decisions to manage and store energy efficiently. All this contributes to reducing dependence on non-renewable sources, minimizing operational costs, and improving the stability of electricity grids — especially in distributed generation and decentralized consumption contexts. He also emphasized the usefulness of AI in designing decarbonization strategies, calculating the carbon footprint of various energy sources, and assessing the environmental impact of technologies such as batteries or solar panels throughout their life cycle.
The implementation of energy digital twins is emerging as one of the most innovative and strategic tools for advancing toward a more efficient, sustainable, and economically optimized energy model. This was highlighted in the presentation “Energy Digital Twins: Efficiency, Sustainability, and Cost Optimization in Energy” by Fernando Mengod and Marcos Carbonell from the Instituto Tecnológico de la Energía (ITE), where they explored how this technology enables real-time simulation, prediction, and optimization of the energy behavior of plants, production processes, and distribution networks. Thanks to advanced artificial intelligence techniques such as recurrent neural networks, computer vision, and time series models, digital twins become valuable tools for predictive maintenance, resource management, production planning, and efficient integration of renewable energies.
Several real-world case studies were presented, demonstrating the transformative potential of digital twins. Among them, the concept of a “green calendar,” which allows industrial production to be scheduled based on the availability of renewable energy, and the automatic grouping of processes with similar water requirements to reduce water and energy consumption. They also showed how it is possible to calculate the carbon footprint associated with each product and process, thus supporting decision-making towards cleaner, more responsible production.
toma de decisiones hacia una producción más limpia y responsable.
