Our AI & Energy research featured in the landmark EU report “The future of European Competitiveness”
Our research on AI in the energy sector was featured in a landmark EU report “The future of European competitiveness”. Mario Draghi's report on the future of European competitiveness, lays out clear recommendations on how Europe can boost its economic growth. A critical component of this is AI and Energy.
See the report here, and the research below:
AI use cases and challenges in the energy sector
AI solutions already provide more than 50 use cases in energy systems today, from grid maintenance to load forecasting, highlighting the versatility and potential impact of the technology. With estimates of the market value for AI applications in the energy sector ranging up to USD 13 billion, the energy sector is one of the sectors with the greatest potential to benefit from the capacity of AI to boost efficiency and accelerate innovation.
Predictive algorithms can be used to forecast energy generation and demand, enhancing the integration of renewables in the energy system. Machine learning aids in aligning variable supply with fluctuating demand, in balancing power generation and loads, and optimizing the value of renewables and grid integration. Moreover, AI-driven insights allow companies to shift peak consumption times, reducing reliance on external power sources and promoting load shifting and ‘peak shaving’ practices.
AI algorithms can support the planning, optimization and predictive maintenance of energy grids, assets and usage. AI aids grid operators in determining system needs based on forecasts of the deployment of additional generation and demand assets, as well as optimal locations for new power infrastructure. AI-enabled schemes can continuously monitor and pre-emptively identify potential faults in energy assets, as well as predict maintenance needs based on historical performance data. AI technologies may also be integrated in building management systems optimizing energy use in buildings and industry, providing a better overall experience to consumers through personalized energy services.
AI can improve energy business decisions, trading and customer relations. Energy companies can use AI algorithms to process real-time pricing data, demand and supply trends, enabling them to make informed and profitable trading decisions. AI solutions can further collect and analyze consumption data, to design better consumer-centric products, such as smart tariffs. Moreover, it can facilitate demand response, as well as empowering consumers to improve their (home) energy management, for example by providing personalized energy use recommendations or energy efficiency upgrades.
To further leverage the power of AI, however, several key factors and measures may be needed to support the uptake of solutions in the electricity grids and the energy sector at large:
Addressing intrinsic challenges posed by AI technologies, especially when applied in critical infrastructures, such as energy. Challenges include data privacy concerns, cybersecurity risks, market manipulation, a lack of accountability when something goes wrong, the traceability of decision making, a lack of transparency and the risk of potential loss of control The EU’s AI Act represents a first step towards tackling these issues
The widespread use of AI comes with a significant increase in energy consumption. In the EU, data centers (incl those needed for AI) are expected to represent over 3% of total power demand by 2030 As these technologies continue to advance, the demand for electricity will sharply increase to power data centers storing vast amounts of data and facilitating complex computations, signaling an increasing need to map the effects of AI’s energy use and wider environmental impacts Today, mainly only big tech companies are investing in computing power to handle AI workloads, primarily using renewable energy, but also other low-carbon sources and solutions like microgrids or advanced software managing energy demand
Factors that might hamper the deployment of AI solutions in energy need to be addressed. The digitalization of the energy system is a prerequisite for the increased use of AI Integrating AI in today’s outdated energy infrastructure is a highly complex task Training AI models requires access to data through interoperability and standardization Furthermore, workers and consumers will need a new set of skills to fully benefit from AI technologies. Finally, a well-functioning ecosystem of innovators, developers and deployers need to be established to ensure the uptake of AI solutions