AI Solutions For Energy Management

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing air-cooled data centers to do liquid cooling without waiting for new construction. And it needed to be rapidly deployed so we could bring customers these powerful AI capabilities while we transition towards facility-level liquid cooling. Think of a home where only one sunny room needs AC, while the rest stays naturally cool – that’s what we wanted to achieve, allowing us to efficiently land both liquid and air-cooled racks in the same facilities with complete flexibility. The available options weren't great. Either we could wait to build specialized liquid-cooled facilities or adopt off-the-shelf solutions that didn't scale or meet our unique needs. Neither worked for our customers, so we did what we often do at Amazon… we invented our own solution. Our teams designed and delivered our In-Row Heat Exchanger (IRHX), which uses a direct-to-chip approach with a "cold plate" on the chips. The liquid runs through this sealed plate in a closed loop, continuously removing heat without increasing water use. This enables us to support traditional workloads and demanding AI applications in the same facilities. By 2026, our liquid-cooled capacity will grow to over 20% of our ML capacity, which is at multi-gigawatt scale today. While liquid cooling technology itself isn't unique, our approach was. Creating something this effective that could be deployed across our 120 Availability Zones in 38 Regions was significant. Because this solution didn't exist in the market, we developed a system that enables greater liquid cooling capacity with a smaller physical footprint, while maintaining flexibility and efficiency. Our IRHX can support a wide range of racks requiring liquid cooling, uses 9% less water than fully-air cooled sites, and offers a 20% improvement in power efficiency compared to off-the-shelf solutions. And because we invented it in-house, we can deploy it within months in any of our data centers, creating a flexible foundation to serve our customers for decades to come. Reimagining and innovating at scale has been something Amazon has done for a long time and one of the reasons we’ve been the leader in technology infrastructure and data center invention, sustainability, and resilience. We're not done… there's still so much more to invent for customers.

How ironic that a single ChatGPT query uses 10x the energy of a Google search, and yet simultaneously, AI is a very powerful tool for greater energy efficiency. As demand for AI surges, the number of data centers is mushrooming. The energy they use will more than double to 945 TWh by 2030, with cooling systems consuming up to 50%. AI is the answer. Our White Space Cooling Optimization helped a customer reduce energy use for cooling, lighting and other peripheral operations by 55%. Digital twins simulate efficiency before construction, while machine learning optimizes cooling in real-time. The data centers powering the AI revolution are being reimagined by the very technologies they enable. When every percentage point of efficiency translates into savings of millions of dollars and tons of carbon, this isn’t just innovation – it’s key for our digital and environmental future. #DataCenters #AI #Sustainability #EnergyEfficiency

Microsoft and Meta Embrace New Power Design for AI Infrastructure: As data center rack densities rise to support more powerful GPUs for AI workloads, power distribution must also evolve. That's why Microsoft and Meta are collaborating on a design that will shift power conversion into a separate rack, laying the groundwork for denser and more configurable server racks. This disaggregated rack design, known as Mt Diablo, will initially use 48Vdc but will enable a shift to a 400Vdc power distribution system for AI data centers. The Mt Diablo project was disclosed at the recent Open Compute Project Foundation summit, and the architectural spec will be contributed to OCP to encourage further collaboration and development. "The need for scalability and future-proofing is driven by high-power server racks, which will exceed a few hundred kilowatts and are moving towards a megawatt," said Microsoft. "Our solution is to separate the single rack into an server rack and a power rack, each optimized for its primary function. With this approach, we can right-size the power shelf count to meet each configuration’s unique needs." The Meta team describes it as "a cutting-edge solution featuring a scalable 400 VDC unit that enhances efficiency and scalability. This innovative design allows more AI accelerators per IT rack, significantly advancing AI infrastructure." The companies say this approach will allow them to deploy 35% more accelerators in each rack, and the shift to 400Vdc will bring greater efficiency as data centers shift to extremely dense AI clusters. Mt Diablo has a modular design to support scalability and future-proofing as server racks grow denser, as well as different power configurations. Here's where you can learn more: Microsoft blog post: https://lnkd.in/e_tcGkEy Meta's blog post: https://lnkd.in/e6UeS86Q Open Compute presentation: https://lnkd.in/emjHAGji

The energy grid is under immense strain from extreme weather, wildfires, and rising electricity demand. As these pressures increase, so does the need for smarter, more resilient and reliable energy grids.   Utilidata, a company that is part of Microsoft's Climate Innovation Fund portfolio, is redefining energy delivery through its AI platform, Karman. This technology empowers utilities to optimize energy delivery and make better decisions about how to manage the grid by, for example, storing electricity in batteries during off-peak hours and distributing it when it's needed. As a result, electric vehicles and solar panels become flexible, valuable assets that help meet grid demand.   Embedding AI directly into the grid infrastructure helps utility decision-makers make more informed decisions and better serve customers. This innovation highlights the power of AI to modernize critical infrastructure and transform the energy sector.

AI has no place in sustainability. There’s a familiar stance I hear a lot in sustainability circles. AI uses a lot of energy. So using it for sustainability sounds… contradictory. But that argument misses the bigger picture. AI isn’t just consuming energy. It’s helping us use less of it too. Used well, AI is already solving real sustainability problems. Not hypotheticals. Not R&D lab demos. Live, operational tools that help businesses reduce emissions, speed up reporting, and make better decisions. Here’s what that looks like in practice: 1. Energy grid optimisation In the UK, the National Grid is using AI to forecast solar energy production by analysing satellite images and weather data. If clouds are expected to lower solar output in, say, Cornwall 30 minutes from now, the grid can prep alternative sources in advance. That means fewer blackouts and lower emissions from fossil backup plants. DeepMind did something similar for wind power. Their AI predicted wind farm output 36 hours in advance, which increased the commercial value of wind energy by around 20 percent. Why? Because energy providers could schedule when to send power to the grid with more certainty. 2. Streamlined carbon accounting AI tools now scan invoices, utility bills and PDF reports to pull out emissions data automatically. They match spend categories to emissions factors and calculate Scope 1, 2 and 3 outputs in seconds. That turns carbon accounting from a once-a-year headache into a real-time management tool. 3. Transparent supply chains Unilever has tested AI platforms that combine satellite imagery with supply data to flag illegal deforestation in palm oil regions. If a patch of rainforest is cleared where it shouldn’t be, AI catches it fast and alerts their team. No need to wait for an audit or third-party tipoff. 4. Faster climate simulations Traditional climate models take weeks or months to run. New AI-driven models can simulate complex climate scenarios up to 25 times faster. That unlocks planning tools for city councils, small businesses and insurers who can’t wait months to model flood risks or heat exposure. Yes, AI needs energy to run. But if it helps avoid 10 times more emissions than it creates, the trade-off makes sense. So the question isn’t whether AI belongs in sustainability. It’s whether we’re serious about using every tool we have to solve the problems in front of us. At Leafr, we’ve seen consultants use AI to cut time and cost on energy audits, validate supplier claims, and surface risks early. When paired with the right human expertise, AI becomes a multiplier. Because the planet doesn’t care if a human or a machine found the emissions. It just cares that they’re found and cut. Follow Gus Bartholomew (Leafr 🌿)for more and repost if you found useful. Use Leafr to find the sustainability specialists you need to support your AI efforts

The next evolution of sustainable AI isn’t just about using more efficient hardware—it’s about Autonomous AI Agents that code with sustainability in mind. These agents are designed to operate independently, learning and adapting as they go, and have the potential to transform software development by writing energy-efficient code. They don't just optimize for speed; they prioritize minimal resource consumption. Why This Matters for Sustainability Modern AI models consume massive amounts of power, yet software development still prioritizes performance over energy efficiency. Agentic AI could change that paradigm by: ✅ Reducing Computational Waste: AI agents could select or generate the most efficient algorithms based on real-time constraints instead of defaulting to resource-heavy models. For example, they could optimize database queries to reduce data retrieval and processing or dynamically adjust resource allocation based on demand. ✅ Automating Green Software Principles: AI-driven frugal coding practices could optimize data structures, reduce redundant calculations, and minimize memory overhead. This could involve choosing the most energy-efficient programming language or framework for a specific task. ✅ Measuring & Optimizing in Real Time: The reward function would be clear: lower energy consumption, less latency, and reduced emissions—all while maintaining accuracy. ✅ Parallel & Distributed Optimization: AI agents could continuously refine codebases across thousands of cloud instances, improving sustainability at scale. AI-Driven Innovation Archive for Green Coding One of the most exciting ideas in autonomous coding is the "Green Code Archive"—an AI-generated repository of energy-efficient code snippets that could continuously improve over time. Imagine: 🔹 Reusing optimized code instead of reinventing energy-intensive solutions. 🔹 Carbon-aware coding suggestions for green data centers & renewable energy scheduling. 🔹 AI-driven legacy refactoring, automating migration to sustainable architectures. Measuring AI’s carbon footprint after the fact isn’t enough—the goal should be AI that reduces energy use at the source. The future of sustainable tech isn’t just about efficient hardware—it’s about intelligent, autonomous software that optimizes itself for minimal environmental impact. While this technology is still emerging, challenges remain in areas like training complexity and robust validation. However, the potential benefits for a greener future are undeniable. Learn more about leading with Agentic AI and its transformative potential in my book, "Empowering Leaders with Cognitive Frameworks for Agentic AI: From Strategy to Purposeful Implementation" (link in the comments section). #agenticai #greenai #sustainability

How can AI help homeowners cut energy bills - without changing their daily routines? At Schneider Electric, we believe the future of energy is not just digital - it’s intelligent. And that future is already here. Our Wiser Home AI Whitepaper explores how artificial intelligence is transforming home energy management. From predictive load shifting to real-time optimization of EV charging and water heating, Wiser Home AI is designed to make homes more resilient, efficient, and sustainable - without compromising comfort. 💡 Why these matters: - Energy prices remain volatile. - Electrification is accelerating. - Homeowners demand smarter, simpler solutions. This whitepaper dives deep into the AI engine behind Wiser, developed in-house by our AI Hub, and shows how it delivers measurable savings - up to 30% by aligning energy use with solar production and dynamic tariffs. Whether you're an energy expert, a tech innovator, or a sustainability leader, this is must-read. 📄 Access the whitepaper here: https://lnkd.in/dBTqxjeV   #WiserHomeAI #Sustainability #AI

Electricity management is increasingly an analytics problem where AI needs to step in. Decarbonization, variable demand, regenerative energy, and complex infrastructure make it impossible to rely on static rules or occasional reporting. Value comes from analyzing operational data continuously and turning it into decisions. The usual analytics setup does not scale. Work is often done in silos, with data pulled into notebooks, results shared as static reports, and little reuse across projects. Domain experts are separated from the analysis, cycles are slow, and each new use case starts largely from scratch. A collaborative model is a catalyst enabling AI to change the economics. At Mitsubishi Electric, data scientists work directly with domain experts on shared workflows. Analytics is used to identify concrete issues and opportunities. In railways, analysis showed where braking generates surplus energy and how it could be reused. In thermal energy management, a full year of building data was analyzed in 20 business days to optimize heating and cooling. Platform efficiency matters. By running the full AI lifecycle in Dataiku, Mitsubishi Electric reduced their time to produce new projects by about 60 percent. That translates into delivering value roughly 2.5 times faster, which means more use cases delivered and quicker operational impact. This is what AI Success looks like in energy and industrial systems. Read the full story on our website: https://lnkd.in/evhhuQNF 

# HPE Chief Technologist's Five-Point Plan to Cut AI Infrastructure Emissions TLDR; Sustainability for AI needs to be planned from the outset and consider the full stack, not bolted on later. Great to see our own John Frey, Senior Director and Chief Technologist for Sustainable Transformation at HPE, interviewed in this article for Capacity Media - a techoraco brand this week. John runs through the five levers of efficiency, and here's my take on them: 1. Equipment efficiency: We typically overprovision and underutilise IT equipment, so consider how to maximise utilisation of the assets you have before adding more capacity 2. Energy efficiency: Maximise performance per Watt of energy consumed, and make use of low power states when resources are idle 3. Resource efficiency: Advanced cooling options like DTC and fanless liquid cooling are more energy efficient than air cooling for power dense workloads. Consider heat recovery to convert waste heat into an asset that can decarbonise other forms of heating 4. Software efficiency: In AI, Python is popular for notebooks and experimentation but as a high-level interpreted language it's also the least energy efficient. Particularly when deploying to production, consider compiled alternatives like Rust or C++ to minimise processor cycles. The Green Software Foundation's Software Carbon Index (SCI) is a useful tool for calculating the carbon impact of software in meaningful terms like number of concurrent users, prompts or tokens 5. Data efficiency: Data exists everywhere and it is inherently messy, it resists our attempts to constrain it into neat boxes. Data strategies need to consider the energy cost of data movement - embracing a hybrid, distributed approach to data management and bringing the AI to the data can significantly reduce unnecessary data movement, loading and duplication. Check out the full interview with John here: https://lnkd.in/eimVfv9d HPE has a long history of building some of the world's most energy efficient AI computers, making use of technical and energy innovations to optimise performance per watt. Now that AI is becoming part of everyone's IT portfolio, efficiency is more important than ever. #sustainableIT #livingprogress #fiveleversofefficiency #ITefficiency

Are we doing enough to make energy affordable and sustainable? As we tackle the demand for energy in a growing world, there’s a pressing question we can’t ignore: How do we ensure that everyone has access to clean, affordable energy without compromising the environment? Sustainable Development Goal #7 is all about addressing this need—ensuring reliable, sustainable, and modern energy for everyone. Take a closer look at how smart technology is transforming the energy landscape. The rise of IoT in renewable energy, for example, has been nothing short of remarkable. Through IoT sensors, we’re not just generating solar or wind power—we’re monitoring, optimizing, and even predicting energy use in real-time. These sensors allow businesses to adjust based on demand, helping to make renewable energy sources more resilient and cost-effective. Consider a business using solar panels or wind turbines to generate its own electricity. With smart grid tech, they can manage power locally, rather than depending solely on a centralized grid. The result? Reduced costs and improved energy efficiency. And it’s not just about generating power; AI and machine learning models help organizations identify peak hours to tap into energy sources efficiently, saving both money and resources. Measuring impact is essential. For many companies, tracking the real-time effects of their energy choices is critical. IoT sensors can monitor energy usage continuously, allowing organizations to prove their progress toward sustainability. By using data instead of manual reports, they can also show customers and employees that they’re taking meaningful action. And then there’s the financial side: How to allocate resources effectively. Data from these smart systems enables leaders to make thoughtful decisions about where to focus their budget. If a particular renewable project shows a greater impact, they can prioritize that effort, optimizing both sustainability and cost efficiency. It’s easy to talk about sustainability, but taking measurable steps—and having the data to back it up—makes a difference. As more organizations embrace these tools, we’re seeing a shift in how companies approach energy, balancing their environmental responsibilities with practical, business-focused strategies. Where do you see your organization on this journey?