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
We were standing in the middle of one of our production halls. Machines humming. People focused. And one laptop screen showing us something crucial: our energy reality. Mathias Weßelmann and I weren’t looking at a dashboard for the sake of it. We were looking at live data from our Energy Management Service Proficloud.io. It didn’t just show consumption—it revealed patterns, inefficiencies, and opportunities. This system connects machines, infrastructure, and buildings into one transparent energy landscape. And ISO 50001 gives us a solid framework for this. But the real value comes when we bring it to life with digital tools. Tools that don’t just collect data, but help us understand where we’re wasting energy, where we’re efficient, and where we can do better. That’s what our Energy Management Service is about. It connects the dots between data, people, and action. Real-time insights allow us to act immediately, not wait for monthly reports. That’s a shift—from reactive to proactive operations. And it supports our sustainability goals without slowing us down. How are you approaching energy management in your operations? Are you using live data or still relying on manual tracking? I’d be interested to hear what’s working for you and where you see room for improvement. Energy efficiency is becoming a strategic capability. Not because it’s required, but because it makes us better. Better at making decisions, better at reducing costs, better at building resilient operations. And that’s exactly what industrial transformation demands. And sometimes, it starts with two people, one laptop, and the willingness to look closer.
🌬️ PART 3: AI, Wind Turbines, and LiDAR Tackling Yaw Misalignment 🛠️ Continuing our exploration of machine learning's role in enhancing wind turbine efficiency, let's talk about a common issue: yaw misalignment. When wind turbines aren't perfectly aligned with the wind, the consequences are two-fold. First, there's a significant dip in energy production, leading to lost revenue. Second, the misalignment causes increased loads on the turbines. This results in higher operational and maintenance costs. Addressing yaw misalignment is crucial for optimizing turbine efficiency and reliability. 🌪️ Understanding Yaw: The Wind Turbine's Compass 🌬️ Imagine the yaw system as the compass guiding a wind turbine, ensuring it faces the wind perfectly. It's like the brain behind the turbine, using a wind vane to detect where the wind is coming from. By adjusting the turbine's direction, it makes sure it's catching as much wind as possible, maximizing energy production. 🌀 Decoding Yaw Misalignment: Static vs. Dynamic 🌪️ Think of yaw misalignment in wind turbines as being off-target, either slightly or because of moving conditions. Static misalignment is like setting up your equipment with a slight offset from the start, due to human error or wear and tear. Dynamic misalignment happens as conditions change, like wind directions shifting, making the turbine sway and struggle to stay aligned. 🌬️ Nacelle LiDAR to the rescue... kind of: 📡 LiDAR technology measures wind speeds before they hit turbine blades, offering a preview that helps adjust the turbine's alignment for optimal efficiency. By detecting wind direction and speed early, LiDAR can fine-tune yaw alignment, reducing wear and enhancing power generation. Despite its benefits, high costs and data accuracy concerns temper widespread adoption. 🎛 Machine Learning + LiDAR = Yaw solution Because LiDAR is an expensive technology, we can leverage ML on real-time data to accurately predict the wind's approach, mimicking LIDAR's precision on a LiDAR-mounted turbine. This approach enhances turbine efficiency by precisely aligning with the incoming wind to maximize energy production and minimize stress. Now calibrating nacelle LiDAR and data extraction is another story. Until next time. Part1: https://lnkd.in/gqt89Q3G Part2: https://lnkd.in/drd8kAft#grid#windturbine#machinelearning#electricalengineering#iot#lidar#energy#energytransition#innovation#yycdata#yyctech#yyc
In the wind industry, where complex logistics and tight margins are a reality, finding smarter ways of working makes a big impact. Our collaboration with Danish startup Claviate shows how AI can transform wind farm installation, traditionally one of our most complex coordination challenges. Using cloud-connected cameras and AI, the “Onsite” system automatically tracks installation progress, compares it with weather and location data, and generates real-time reports - replacing manual, anecdotal inputs with factual insights. The results are impressive: our site managers can now spend less time on paperwork and more time focusing on what matters - safety, quality, and operational excellence. At our Berglicht site, this led to zero escalations and faster issue resolution. This is exactly the kind of practical innovation our industry needs - solutions that create tangible value by improving efficiency and collaboration between partners. I'm pleased to share this story of how we're making wind energy installation smarter, safer, and more efficient. Read the full article below. #WindIndustry#AI#CleanEnergy
𝐀𝐈: 𝐏𝐨𝐰𝐞𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐧𝐞𝐫𝐠𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 Energy is the lifeblood of process manufacturing, yet the way we manage it often feels more reactive than strategic. As industries face increasing pressure to optimize resources, Applied AI is revolutionising how manufacturers manage energy in process manufacturing. Here’s how we are using Applied AI to reshape the landscape: 👉 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐢𝐧 𝐄𝐧𝐞𝐫𝐠𝐲 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: AI-driven systems analyze energy consumption in real time, identifying inefficiencies at the machine level to boost operational efficiency and cut waste. 👉 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐄𝐧𝐞𝐫𝐠𝐲 𝐃𝐞𝐦𝐚𝐧𝐝 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠:Harnessing the power of machine learning, manufacturers can forecast energy needs based on production schedules, historical data, and external factors such as market trends and weather conditions. This proactive approach ensures facilities use only what’s necessary—resulting in potential savings of up to 15% on energy bills. 👉 𝗘𝗻𝗲𝗿𝗴𝘆 𝗰𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: AI systems can analyze historical energy consumption data and identify patterns that may lead to inefficiencies. By implementing AI-driven solutions, manufacturers can optimize their energy usage by adjusting processes based on real-time data. 👉 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐑𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞 𝐄𝐧𝐞𝐫𝐠𝐲: As the transition to greener energy sources accelerates, AI plays a crucial role in optimizing their integration into manufacturing processes. It enables seamless operations, effectively managing intermittent renewable energy sources like solar power and ensuring smooth transitions between various energy supplies. 𝗛𝗼𝘄 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗲𝗿𝘀 𝗖𝗮𝗻 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗘𝗻𝗲𝗿𝗴𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁? ✅ 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐒𝐦𝐚𝐫𝐭 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: AI thrives on data, making it essential to upgrade to smart sensors and IoT-enabled systems. Collecting high-quality data on key parameters is the first step toward unlocking AI’s potential for actionable insights. ✅ 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬: By creating a virtual replica of manufacturing processes, digital twin technology powered by AI allows organizations to simulate energy scenarios. This enables optimization for efficiency without disrupting ongoing operations. ✅ 𝐀𝐝𝐨𝐩𝐭 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: With the right tools, energy costs can become predictable. Predictive analytics equip manufacturers to forecast fluctuations in energy prices and usage, enabling proactive adjustments that enhance financial performance. At Ingenero, we believe that Applied AI is the key to bridging the gap between efficiency and sustainability in process manufacturing. To my peers in process manufacturing: How are you leveraging AI to transform your energy management? #EnergyManagement#Processmfg
When Jim Gao first saw #AlphaGo, he went to Demis Hassabis at Google DeepMind with a bold proposal: let reinforcement learning run wild on Google's data centers. Demis accepted, and the results were astounding: 40% data center energy savings for Google. One of my favorite episodes of Sequoia Capital Training Data w/ Pat Grady drops today on how Jim led DeepMind energy, one of the first and only applications of true deep reinforcement learning in the real world, and then founded Phaidra. Listen in for a thrilling recount of the DeepMind energy story and hear Jim's thoughts on which real-world applications are most poised to be transformed by reinforcement learning (as opposed to transformers and other AI technologies).
The weather. A small talk topic for many, a (main) source of info during skiing season for others (🙋). And for our Hywind Tampen team: a challenge. When the wind blows at Hywind Tampen, the world’s first – and largest floating wind farm, Equinor produces renewable energy for the Gullfaks and Snorre field platforms. That way, the use of traditional gas-powered turbines offshore are reduced – and so are our CO2 emissions. But the wind is unpredictable. Luckily, to artificial intelligence (AI), the weather and wind – like anything else, are only data. Data that can be structured and used to solve challenges. Sure, AI won’t produce any wind (or snow, for that matter). But by using our own data and a machine learning algorithm, Equinor have now developed a wind and weather prediction solution. This AI solution is based on historical and current weather data, and not least real-time wind measurements on our own installations in the area. It was launched late this October. When the wind-measurements are telling us that the wind is blowing a few kilometers away, and the direction is right, we can expect wind at Hywind Tampen, even if the forecast says no. This makes the Hywind Tampen team (that operates the facility from onshore from Bergen) better at predicting and calculate just how much power we will get from the wind within the next 1-2 hours. What does it mean in practice? That we can beat traditional weather forecasts, in a way. And reduce the amount of idling power generators to a minimum. I love the fact that we see more and more concrete examples on how AI is being used to optimize operations and solutions, like this one. Let me know below if you have other great examples 😀
⚡ Traditional rack solutions integrate power and server infrastructure in a single rack, but this approach limits the number of AI accelerators. As the power requirement for servers grows, separating these into a server rack and a disaggregated power rack is appealing. This modular design allows for better space optimisation, increasing the space available for AI accelerators by up to 35%. Moreover, the approach is scalable, and the same racks can be used easily around the globe. 🔋 The disaggregated power rack can easily adjust to the new 400V DC-powered AI chips. The higher voltage will also improve efficiency and create a future-proof infrastructure by standardising connectivity solutions, power rack dimensions, and safety standards across the industry. 🔦 Adopting a higher voltage will also reduce the amount of copper needed as the cable diameter decreases as the voltage increases for the same power delivery. For a 500-kW rack: - At 48V, the diameter is 52 mm. - At 400V, the diameter reduces to 20 mm. - At +/- 400 (effective 800V), the diameter further decreases to 14 mm. #microgrid#powerelectronics#lowvoltagedc#solidstatecircuitbreaker#dc#datacenter#ai#cleanenergy#sscb
𝐓𝐢𝐦𝐞-𝐨𝐟-𝐃𝐚𝐲 𝐓𝐚𝐫𝐢𝐟𝐟𝐬: 𝐀 𝐒𝐭𝐫𝐨𝐧𝐠 𝐃𝐫𝐢𝐯𝐞𝐫 𝐟𝐨𝐫 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐄𝐧𝐞𝐫𝐠𝐲 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 !!!⚡🔋 As India’s power sector evolves, 𝐓𝐢𝐦𝐞-𝐨𝐟-𝐃𝐚𝐲 (𝐓𝐨𝐃) 𝐭𝐚𝐫𝐢𝐟𝐟𝐬 are becoming a key reform to promote efficient energy use, reduce peak demand stress, and enhance grid stability. With higher tariffs during peak hours and lower tariffs during off-peak periods, consumers now have a compelling incentive to adopt smarter energy management strategies. This is exactly where 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐄𝐧𝐞𝐫𝐠𝐲 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐁𝐄𝐒𝐒) create transformational value. 𝐇𝐨𝐰 𝐁𝐄𝐒𝐒 𝐇𝐞𝐥𝐩𝐬 𝐂𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬 𝐁𝐞𝐧𝐞𝐟𝐢𝐭 𝐟𝐫𝐨𝐦 𝐓𝐨𝐃 𝐓𝐚𝐫𝐢𝐟𝐟𝐬 ✅ ■ Charge batteries when electricity is cheaper (off-peak hours) ■ Discharge stored energy during peak hours to avoid higher ToD tariffs ■ Lower monthly electricity bills through intelligent load shifting ■ Improve reliability with backup power during outages ■ Maximize solar utilization by storing excess daytime generation for evening use With ToD tariffs being widely introduced by DISCOMs across India, the economics of BESS are becoming even more favourable, especially for commercial and industrial consumers with significant peak-hour demand. 𝐌𝐚𝐡𝐚𝐫𝐚𝐬𝐡𝐭𝐫𝐚 𝐔𝐩𝐝𝐚𝐭𝐞: 𝐌𝐒𝐄𝐃𝐂𝐋’𝐬 𝐌𝐮𝐥𝐭𝐢 𝐘𝐞𝐚𝐫 𝐓𝐚𝐫𝐢𝐟𝐟 (2025–30)⚡ Effective July 2025, MSEDCL has introduced, revised ToD zones (timings), a larger gap between solar hours and high-peak hours. These changes further strengthen the case for BESS by enabling attractive energy arbitrage (load shifting) opportunities. Below is a snapshot of the HT consumer ToD changes and their impact on per-unit charges, highlighting why storage-based optimization will become increasingly beneficial. Pairing 𝐒𝐨𝐥𝐚𝐫 𝐏𝐕 + 𝐁𝐄𝐒𝐒 creates a resilient, cost-optimized, and sustainable energy ecosystem that aligns perfectly with India’s clean energy transition goals. Stay tuned for insights on, rising demand charges, how BESS can offset these costs, the emerging business case for large-scale adoption. 📌 𝘉𝘺 𝘪𝘯𝘷𝘦𝘴𝘵𝘪𝘯𝘨 𝘪𝘯 𝘴𝘮𝘢𝘳𝘵 𝘴𝘵𝘰𝘳𝘢𝘨𝘦 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯𝘴 𝘵𝘰𝘥𝘢𝘺, 𝘸𝘦 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘰𝘯𝘭𝘺 𝘳𝘦𝘥𝘶𝘤𝘪𝘯𝘨 𝘦𝘯𝘦𝘳𝘨𝘺 𝘤𝘰𝘴𝘵𝘴 𝘣𝘶𝘵 𝘢𝘭𝘴𝘰 𝘴𝘵𝘳𝘦𝘯𝘨𝘵𝘩𝘦𝘯𝘪𝘯𝘨 𝘨𝘳𝘪𝘥 𝘴𝘵𝘢𝘣𝘪𝘭𝘪𝘵𝘺, 𝘦𝘯𝘢𝘣𝘭𝘪𝘯𝘨 𝘥𝘦𝘦𝘱𝘦𝘳 𝘳𝘦𝘯𝘦𝘸𝘢𝘣𝘭𝘦 𝘪𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘪𝘰𝘯, 𝘢𝘯𝘥 𝘤𝘰𝘯𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘯𝘨 𝘵𝘰 𝘰𝘶𝘳 𝘧𝘪𝘨𝘩𝘵 𝘢𝘨𝘢𝘪𝘯𝘴𝘵 𝘤𝘭𝘪𝘮𝘢𝘵𝘦 𝘤𝘩𝘢𝘯𝘨𝘦 𝘧𝘰𝘳 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘴 𝘵𝘰 𝘤𝘰𝘮𝘦. 🌍 #EnergyStorage#BESS#EnergyArbitrage#SolarEnergy#CleanEnergy#RenewableEnergy#MSEDCL#Sustainability