According to Dr Violeta Motuzienė, professor at Vilnius Gediminas Technical University and a member of the SustAInLivWork project, the energy sector’s transition to electrification, particularly with the rise of electric vehicles and heat pumps, is placing unprecedented strain on electricity grids. These systems must now handle not only increased demand but also the variable nature of renewable energy sources like wind and solar.
“As a result, electricity grids are facing major challenges in terms of their capacity and flexibility to accommodate the growing demand for electricity and the integration of renewable energy sources (RES) into the grid to ensure a secure and reliable supply of energy,” she says.
This growing complexity, where end-users can also act as producers (prosumers), calls for smarter, data-driven solutions.
AI Enhancing Grid Reliability and Efficiency
As Prof. Dr Motuzienė highlights, “The use of innovative grid technologies using AI can improve grid reliability by providing predictive maintenance, load forecasting, and real-time grid monitoring.”
Predictive maintenance systems, powered by AI, can foresee and mitigate equipment failures before they occur, ensuring reliability in the grid. Additionally, AI-based load forecasting can help balance supply and demand in real-time, optimising the use of renewable energy and reducing waste. By integrating real-time data and historical patterns, AI enhances grid flexibility, allowing for more efficient energy distribution and minimising disruptions.
Meanwhile, in district heating (DH) systems, in the context of the transformation of the DH sector to a 4th generation network, Prof. Dr Motuzienė sees the application of AI as an untapped potential rather than a challenge.
“In the DH system, the AI can be also applied in all the stages of the value chain – generation, distribution, heat substations, and consumption for different purposes – demand and load forecasting, optimisations, network design, analytics, and even increase of the awareness,” she notes.
AI and Smart Cities: Technology for Human Well-being
Smart cities represent the future of urban development, where technology plays a key role in enhancing sustainability. AI is a cornerstone of this vision, especially in energy management.
“By its very nature, the smart city concept is about improving services to urban populations through digitisation and Big Data, and energy is a critical service that needs to be delivered,” says Prof. Dr Motuzienė.
In these cities, AI can optimise energy grids by predicting consumption patterns, efficiently integrating renewable energy sources, and reducing energy waste.
“AI, IoT (Internet of Things), and Big Data analytics play a crucial role in optimising energy grids (dynamic energy allocation by predicting demand and efficiently integrating renewable energy sources, reducing waste), improving energy efficiency (AI-based automation of buildings to reduce energy consumption and increase energy transport efficiency), decentralising energy systems (AI will facilitate peer-to-peer energy trading, utilising local production and storage) and predictive maintenance (anticipating failures in energy infrastructure),” asserts Prof. Dr Motuzienė.
Ethical Considerations in AI-Driven Energy Solutions
While AI presents transformative potential, it also brings ethical challenges. The energy sector, critical to human well-being, must ensure that AI applications do not compromise security or privacy.
“The main ethical key considerations when applying AI in the energy sector are related to cybersecurity threats, as AI-powered energy systems are vulnerable to cyberattacks that could disrupt energy supply and other essential services that require it,” warns Dr Motuzienė.
Ensuring robust cybersecurity measures and protecting against threats is paramount, especially as energy grids become more interconnected and reliant on digital technologies. Data privacy is another significant concern.
“In addition, AI tools collect and manage monitoring data, which can lead to invasive data collection that violates individual privacy. It is very important to ensure that data is depersonalised,” Prof. Dr Motuzienė emphasises.
Building AI Expertise for the Future
The integration of AI into the energy sector also requires a new set of skills and knowledge. Prof. Dr Motuzienė stresses the need for basic and advanced training in AI, particularly for those working with big data.
“I can only rely on my personal experience and communication with colleagues in Lithuania and abroad. Most of the very good energy professionals know their field well but artificial intelligence is still a big novelty for them, and it seems to be something very complicated, apart from the well-known tools like ChatGPT and similar tools. The ability to use AI must become as natural as basic computer skills,” she suggests.
Looking ahead, Prof. Dr Motuzienė is optimistic about the role AI will play in decarbonisation of the energy sector. Her research focuses on incorporating AI into building lifecycle to enhance energy efficiency and accelerate renovation processes.
“We still see great potential for the use of AI in buildings, for example to speed up slow building renovation processes by providing stakeholders with AI-based decision support tools,” she notes.
By leveraging interdisciplinary expertise, Prof. Dr Motuzienė aims to develop AI-based solutions that will significantly reduce the carbon footprint of buildings and improve the overall energy efficiency of the sector.
Kaunas University of Technology (KTU), Vytautas Magnus University (VMU), Vilnius Gediminas Technical University (VILNIUS TECH) and Lithuanian University of Health Sciences (LSMU), together with international partners University of Hamburg (TUHH) and Tampere University (TAU), are implementing the project SusAInLivWork, which is developing a Centre of Excellence (CoE) for Sustainable Living and Working. The project is co-funded under the European Union’s Horizon Europe programme under Grant Agreement No. 101059903 and under the European Union Funds’ Investments 2021-2027 (project No. 10-042-P-0001).
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