The Hidden Power of AI: How Smart Technology is Reshaping Global Energy Systems

Can AI Transform Our Energy Future Through Data?

Energy systems today generate unprecedented volumes of data—from smart buildings tracking thousands of variables every minute to wind farms producing billions of data points annually. AI has become indispensable in managing this complexity, enabling real-time forecasting, predictive maintenance, and grid optimization. As power systems evolve into decentralized, multi-directional networks, AI is emerging as a key driver of efficiency and sustainability.

Traditionally defined by centralized power generation, today’s power systems are evolving into decentralized, multi-directional networks fueled by a surge in grid-connected devices. In this complex digital landscape, AI has emerged as a critical tool. By processing massive data streams, AI enables utilities to forecast supply and demand, optimize grid operations, and perform predictive maintenance, all while driving breakthrough innovations such as enhanced renewable energy forecasting and advanced climate modeling.

AI’s Role in Reducing Global Emissions

AI-driven solutions have the potential to significantly reduce emissions. According to a Boston Consulting Group (BCG) report, scaling existing AI applications could cut global greenhouse gas emissions by up to 10% by 2030—contributing meaningfully to net-zero goals¹. Complementary research from the Fraunhofer Institute maps nine distinct fields of AI application across the energy value chain, which can be organized into three key clusters²:

1. Strategic Decision-Making – Optimizing investments, grid planning, and energy trading.
2. Infrastructure Resilience – Enabling predictive maintenance and strengthening power grids.
3. Consumer & Market Integration – Customizing energy products, automating billing, and empowering consumers with real-time insights.

Further reinforcing the transformative potential of digital technologies, an analysis by the World Economic Forum and Accenture indicates that scaling these innovations could reduce emissions by up to 20% by 2050 in the energy, materials, and mobility sectors. Achieving these reductions depends on data transparency, digital talent, and robust cross-sector partnerships. For instance, AI-powered cloud computing and 5G networks in the energy sector could deliver up to an 8% reduction in greenhouse gas emissions by 2050².

AI Start-ups Reshaping the Energy Sector

A new wave of AI-driven start-ups is redefining how we manage energy. With nearly one third of all SET100 List 2025 start-ups focusing on AI. Companies like Aedifion, R8 Technologies, and ProptechCore use digital twins and real-time analytics to optimize HVAC systems, cutting energy use by up to 50%. In grid management, Rabot Charge and Electric Miles help balance supply and demand through AI-driven dynamic pricing and smart EV charging. Meanwhile, firms like Arkion Solutions and Enline employ machine learning and sensorless digital twins to predict infrastructure failures, improving grid reliability while lowering maintenance costs. Across all sectors, these innovations make energy systems more adaptive, cost-effective, and sustainable.

While AI accelerates the clean energy transition, it also introduces challenges. Training large AI models requires substantial computing power, with data center energy consumption expected to rise sharply. Furthermore, AI decision-making in energy markets must guard against biases that could disadvantage certain regions or consumers. To ensure AI’s environmental benefits outweigh its footprint, innovators are focusing on low-power algorithms, renewable-powered data centers, and responsible AI governance. The goal is clear: an energy-smart future must also be an energy-efficient one.

According to a 2025 Federal Ministry for Economic Affairs and Climate Action analysis, data center energy consumption in Germany alone will quadruple from 20 TWh to approximately 80 TWh by 2045, driven significantly by AI applications³. While computation efficiency per watt continues to improve, the absolute power consumption of AI systems is rising substantially. This challenge is inspiring new start-ups devoted to low-carbon computing, advanced optimization, and the responsible use of AI. The goal is to ensure the benefits of AI and digitalization far outweigh their environmental costs while supporting grid stability and sustainability through innovative approaches to power management and efficiency.

Ready to witness the future of energy? Explore the SET100 2025 AI start-ups and discover how they are shaping an efficient, resilient, and sustainable energy ecosystem—one breakthrough at a time.

AI in Action: How SET100 Start-ups Are Shaping the Future

Building Energy Management & HVAC Optimization:

A significant segment of the SET100 AI start-up landscape is dedicated to transforming existing buildings into smart, adaptive, and low-carbon environments. These companies leverage data analytics, digital twins, and predictive control to optimize energy use and improve indoor comfort without requiring costly retrofits – a key innovation in the built environment.

Aedifion exemplifies this retrofit-first approach, using edge devices and generative AI to test thousands of energy-saving scenarios in virtual clones of buildings. These digital twins learn from real-time data—HVAC performance, room occupancy, weather patterns—to adjust settings automatically, achieving up to 40% energy reduction. Similarly, R8 Technologies and ProptechCore (decarbAI) have developed solutions that continuously learn building dynamics and adjust HVAC operations, achieving energy savings that in some cases reduce consumption and emissions by up to 50%. For commercial buildings, syte and vilisto integrate self-learning algorithms into intelligent thermostats and real-time management systems, ensuring that energy is used only when needed. Thermosphr uses physics-based AI models to optimize energy efficiency in industrial heating and cooling systems, while WeavAir‘s digital twins enable up to 30% emission reduction through smart building management. At the residential level, Plentify‘s HotBot AI optimizes water heaters and solar systems based on learned usage patterns, cutting energy bills by 24%. BioEsol brings similar optimization to SMBs, combining AI with battery storage to reduce costs and increase renewable integration.

Grid & Energy Aggregation:

At both infrastructure and consumer levels, AI is transforming how energy flows between producers and consumers through the grid.

Rabot Charge enables dynamic pricing based on real-time conditions, helping balance supply and demand through market mechanisms. Electric Miles‘ AI-driven platform shifts EV charging to off-peak times, cutting costs by 24% and supporting grid stability – demonstrating the crucial link between EV adoption and grid management. Moving beyond individual assets, Podero and Skoon Energy create sophisticated marketplaces for energy storage, using AI to match supply with demand and optimize deployment. Through community-scale approaches, Allye Energy‘s platform coordinates shared batteries, making storage 100x more affordable than individual solutions. Ostrom demonstrates how AI can orchestrate diverse energy assets into virtual power plants, enhancing grid flexibility while reducing costs. This integration of distributed resources shows how AI can transform passive consumers into active participants in the energy system. REplace further supports grid development by using AI to analyze over 40 parameters for optimal solar site selection, addressing the critical 80% failure rate in renewable project development. Like other platforms in this space, it’s making the energy system more efficient from the ground up by enabling data-driven decision making.

Infrastructure Inspection & Maintenance:

Ensuring the reliability of energy infrastructure is another domain where AI is making a significant impact. Start-ups in this cluster focus on data from sensors, drones, and simulations to predict and prevent failures.

Arkion Solutions, Sentrisense, and Pix Force employ machine learning algorithms to analyze images, LiDAR scans, and thermal data, spotting problems in power lines before they cause outages. Enline‘s sensorless digital twin approach takes this further by simulating grid behavior without additional hardware, enabling predictive maintenance and optimization. Werover brings innovation through sound analysis – their AI processes acoustic data to detect early signs of wind turbine blade damage, achieving 50% cost reduction in repairs and 70% reduction in downtime. Airteam‘s AI transforms drone-captured images into precise 3D models, slashing traditional measurement times by 90% while maintaining centimeter-level accuracy. HyLight brings another innovative approach with their hydrogen-powered airships that can inspect 350km of infrastructure in a single carbon-free flight. While Werover uses sound and Airteam uses drones, HyLight’s HyLighter combines long-range capabilities with zero emissions, offering a sustainable alternative to traditional helicopter inspections. SeekOps complements these approaches with AI-enhanced sensor systems that detect methane leaks at industrial sites, providing critical data for both environmental compliance and asset management.

These predictive maintenance approaches not only reduce operational costs but also help utilities comply with increasingly stringent reliability and safety regulations.

Industrial Process Optimization:

Industrial sectors, traditionally energy-intensive and hard-to-abate, are increasingly turning to AI for process optimization and emissions reduction. Carbon Re has taken on one of the most challenging sectors—cement manufacturing—using an AI-based control platform to optimize fuel usage and reduce emissions on a gigatonne scale. Their solution helps industries not only reduce their environmental impact but also navigate emerging carbon pricing regimes more effectively. Similarly, encentive has developed flexOn, an AI-powered energy management platform that helps industrial companies align their energy consumption with renewable availability, while etalytics deploys its etaONE® platform to deliver real-time optimization and predictive analytics for industrial energy systems. These solutions demonstrate how AI can make existing industrial infrastructure more sustainable without compromising productivity. Variolytics extends this approach to wastewater treatment, providing an AI-based Emission Control System that reduces GHG emissions while optimizing plant operations.

Smart Mobility & EV Logistics:

Mobility start-ups are applying AI to enhance the efficiency and sustainability of transportation networks, particularly in the rapidly growing electric vehicle sector. Blitz and Fin use real-time data analytics to optimize EV fleet routes and charging strategies, reducing delivery times by 35% and 30% respectively while cutting emissions. Their platforms incorporate features such as dynamic driver allocation and demand forecasting that not only reduce operational costs but also lower carbon emissions by maximizing renewable energy use. The synergy between fleet optimization and grid management is particularly evident in these solutions, as AI helps balance vehicle charging needs with grid capacity. Volvero combines AI with smart contracts to create efficient vehicle-sharing ecosystems, while Kazam adds another layer by offering AI-driven logistics for EV charging networks. Together, these companies demonstrate how AI can accelerate the transition to electric mobility while managing its impact on the grid.

A Data-Driven Future for Energy

All these game-changing start-ups point to one trend: data-driven intelligence is changing the energy landscape. Philipp Richard, Head of Digital Technologies & Start-up Ecosystem at dena, highlights AI’s pivotal role in the energy transition:

He continues: ‘The ability of AI to process vast datasets, recognize patterns, and make intelligent decisions in real time is becoming indispensable. It plays a key role in integrating renewables, enhancing grid stability, and improving energy efficiency. However, scaling these solutions requires collaboration between start-ups, industry leaders, and policymakers to fully unlock AI’s potential across global markets. The dominance of AI-driven innovations in this year’s SET100 selection highlights how digital intelligence is not just optimizing energy systems—it’s fundamentally transforming them. These start-ups are proving that AI is a catalyst for a more efficient, resilient, and sustainable energy future.’

The Road Ahead: Smarter Energy, Not Just New Hardware

The future of energy isn’t about replacing old systems with new hardware—it’s about making existing infrastructure smarter. AI is unlocking a real-time, adaptive energy ecosystem where grids self-balance, buildings adjust consumption autonomously, and industries optimize processes for efficiency. However, ensuring that AI’s environmental benefits outweigh its resource demands requires a responsible approach—focusing on efficiency, transparency, and collaboration. The energy transition won’t be defined by a single breakthrough but by thousands of connected, intelligent decisions shaping a more resilient and sustainable world.

Want to see AI in action? Check out the SET100 2025 start-ups leading the charge in energy innovation.

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Sources

1. BSG report, How AI Can Speed Climate Action (2023): https://www.bcg.com/publications/2023/how-ai-can-speedup-climate-action
2. Klobasa, Marian; Plötz, Patrick; Pelka, Sabine; Vogel, Lukas (2019): Artificial intelligence for the integrated energy transition. Assessing the technological status quo and categorising fields of application in the energy system. Karlsruhe: Fraunhofer ISI. 
3. World Economic Forum, Digital solutions can reduce global emissions by up to 20%. Here’s how (2022): https://www.weforum.org/stories/2022/05/how-digital-solutions-can-reduce-global-emissions/ 
4. Deutsche Energie-Agentur. Stand und Entwicklung des Rechenzentrumsstandorts Deutschland: Gutachten im Auftrag des Bundesministeriums für Wirtschaft und Klimaschutz. 17 Jan. 2025, https://www.dena.de/projekte/deutschland-als-standort-fuer-rechenzentren/.