AI Solutions For Energy Management

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.

Discover Senseye Predictive Maintenance live from Transform 2024! Ryan Falcini walks us through the key elements of the Senseye Predictive Maintennace platform covering: ❓ What is Senseye?: Senseye is a cloud-based AI and machine learning tool designed to detect and alert users to potential machine failures and forecast breakdowns. It is industry-agnostic, supporting various sensors and technologies. ⚙️ Primary Use: Senseye acts as a decision support tool, guiding users on maintenance priorities through the Attention Index. This index uses a traffic light system (green, yellow, red) to indicate priority levels for asset issues. 👩🏻💻 User Interaction: Users receive detailed cases highlighting anomalies or trend detections, showing specific measures causing concern. Feedback from users helps fine-tune algorithms and improve the Senseye experience. 💻 Advanced Capabilities: Senseye employs generic AI to offer prescriptive guidance, beneficial for organizations lacking the expertise to interpret complex data. Language learning models provide actionable checklists to restore asset health. 🤓 Main Goal: The primary objective is delivering the right information to the right person at the right time, preventing unplanned downtime and reducing maintenance costs. #PredictiveMaintenance #Transform2024 #Industry40

𝐑𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞𝐬 𝐚𝐫𝐞 𝐨𝐧 𝐭𝐡𝐞 𝐫𝐢𝐬𝐞 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐠𝐫𝐢𝐝 𝐬𝐮𝐟𝐟𝐞𝐫𝐬. 𝐂𝐚𝐧 𝐀𝐈 𝐡𝐞𝐥𝐩 𝐮𝐬 𝐫𝐞𝐬𝐨𝐥𝐯𝐞 𝐭𝐡𝐞 𝐢𝐬𝐬𝐮𝐞𝐬 𝐭𝐡𝐚𝐭 𝐩𝐚𝐫𝐭𝐢𝐚𝐥𝐥𝐲 -𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐯𝐚𝐬𝐭 𝐩𝐨𝐰𝐞𝐫 𝐜𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧- 𝐜𝐫𝐞𝐚𝐭𝐞𝐬? EMBER says wind and solar outpaced EU fossil fuel production in H1 2024. For the first time, wind and solar generated 30% of EU electricity, surpassing fossil fuels. However, power infrastructure constraints limit Europe's wind and solar energy growth. Electricity grids waste #renewable energy. Transmission networks supply most data for centralized, stable electrical grids without analysis or prediction. Utility companies rarely gather real-time windspeed, line temperature, voltage, and frequency data, hindering renewable energy integration. Estimate peak or #solarpower generation by tracking network-wide wind and temperature. Some grids feature extensive blind spots. Traffic and blind spots waste #energygrid capacity, so #utilities cannot swap excess capacity or use all renewables during peak hours. Instead of monitoring line temperatures and local weather in real time, many utilities set safe capacity limitations using crude, overcautious calculations, which may underutilize the system. Flexible networks are needed to connect intermittent renewable power sources with power capacity awareness. European and US PV and wind rates update every few minutes. Accurate system capacity, generation, and transmission linkages will lower power prices. With multi-sensing (#IoT) grid monitoring systems, old grids can become AI-enhanced systems that detect multi-point electrical, physical, and environmental phenomena like voltage, frequency, harmonics, cable ampacity, temperature, and wind speed. ML uses this extensive data set to adapt network capacity and renewable power sources to the weather set. Innovative technologies boost renewables and cut power loss. Weather and cable temperatures assist #ML systems in anticipating network safety months ahead. Network operators can securely add capacity and renewable energy at night or in better mountainous locations. Parallel lines share loads to boost capacity and predict demand. The new wave of #AI may boost renewables. Weather-related renewable power sensor data, mostly scattered, could anticipate capacity increases. #Utility operators can forecast solar and wind peak production and use cheap, clean #power. Power theft and loss decrease with renewables. AI-based location-based fault detection systems could secure networks and conserve clean #electricity by detecting power leaks and theft. Data-driven network designs boost capacity, save electricity, and integrate renewable #energy early for security. Machine learning algorithms may recommend new wire cooling, capacity, or energy-conducting materials network areas. AIs predict power-saving network designs and locations, boosting #cybersecurity.

💥 Agentic AI Unleashes the Green Revolution: How Generative Workflows Will Power the Energy Sector Generative AI is no longer just about creating stunning images or crafting compelling text. A new paradigm can alter energy industry - generative AI agentic workflows. Imagine AI not just as a tool for analysis or content creation, but as an autonomous agent, capable of generating solutions, orchestrating actions, and driving complex processes from end-to-end. Nowhere is this transformative potential more profound than in the energy sector, a domain crying out for innovation and efficiency. ✅ What exactly are these agentic workflows? They combine the creative power of generative AI with the proactive execution of intelligent agents. Think of it as AI that can not only imagine optimal energy solutions but also autonomously implement them. These workflows are designed to handle complex, multi-step processes, learn from experience, and adapt to dynamic environments, pushing automation beyond simple rule-based systems ✅ Why is this a game-changer for energy? Because the energy sector faces immense challenges: meeting growing demand, transitioning to renewables, optimizing vast and complex grids, and the like. Generative AI agentic workflows offer a powerful toolkit to tackle these head-on. Let's dive into specific examples of how this will unfold: 🛫 Hyper-Personalized Energy Savings Agents for Consumers: Forget generic energy-saving tips. Agentic AI can analyze a household’s specific energy consumption patterns, appliance usage, and even lifestyle habits. Based on this deep dive, it generates truly personalized energy-saving recommendations – and crucially, it can autonomously implement them. Imagine an AI agent that learns your preferred home temperature, analyzes energy pricing fluctuations, and then subtly adjusts your smart thermostat and appliance schedules to minimize your bill without impacting comfort. 🛫 Predictive Maintenance Agents for Energy Infrastructure: Power plants, wind turbines, and pipelines require constant maintenance to prevent costly failures. Agentic AI can continuously monitor sensor data from these assets, generate predictive maintenance schedules based on subtle anomaly detection, and even autonomously trigger maintenance workflows. This minimizes downtime, extends asset lifespan, and improves the overall reliability of energy infrastructure. ☑️ The implications are staggering: a more resilient, efficient, and sustainable energy sector, powered by AI agents working autonomously to optimize every facet of energy generation, distribution, and consumption. While challenges like data security, ethical considerations, and job displacement need careful consideration, the potential of generative AI agentic workflows to drive a transformation in the energy sector is undeniable. #slb #ai #genAI #energy #tech

Data is crucial for automation. 📊 The video below shows how we acquired data at our pilot partner's site to feed into our AI platform and automate HVAC controls, achieving 65% energy savings with simple sensors. 💡 Although we demonstrated the energy-saving potential of the FLUIX AI Autopilot system in this test environment, we do not install sensors in our clients' facilities. 🏭 Typically, critical infrastructures such as data centers and manufacturing facilities already have the necessary energy and temperature sensors. However, these sensors are often disparate and isolated. 🔌❄️ If sensors are missing, we collaborate with sensor partners to implement data capture. Our AI Autopilot aggregates all sensor and control data into one platform and autonomously manages facility systems (HVAC, IT, water, etc.) to save energy. 🌐⚡ Read more about our recent case study where we cut HVAC energy use by 65% in our pilot's server room: https://lnkd.in/eh43mbTd #DataCenters #AI #Automation #EnergyEfficiency #hvac #controls #fluix #aimi #Sustainability #Tech #FacilityManagement

Planning energy transitions without sufficient data is like trying to navigate in the dark.   Despite decades of progress, over 685 million people still lack access to electricity. Traditional data sources - household surveys, national censuses, static infrastructure maps - are too slow, too sparse, or too disconnected from on-the-ground realities to be able to accurately make investments and optimize projects.   To address this, UNDP partnered with IBM to co-develop two data-driven tools now featured in the International Energy Agency (IEA) ’s new Energy & AI Observatory👉🏾 https://lnkd.in/e3yJs_Q4. These models represent a digital shift, as AI and open data enable a just energy transition through grounding data-driven actions in approaches that leave no one behind:     1. Clean Energy Equity Index
Developed with IBM and Stony Brook University, this tool generates an equity score at the subnational level across 53 African countries, combining data on education, income, emissions, and infrastructure. The index helps identify regions where clean energy investment will have the most equitable and transformative impact.     2. Electricity Access Forecasting Model
Built with IBM watsonx and trained on satellite imagery, infrastructure data, population growth, and land use dynamics, this model delivers hyper-granular (1 km²) forecasts to 2030 across 102 Global South countries. It enables governments to anticipate demand and prioritize underserved areas long before gaps become crises.   Both tools are now accessible through GeoHub, UNDP’s open data platform for geospatial intelligence. https://lnkd.in/erh3Qmny. Moving forward, the challenge will be how we can embed these tools into institutional decision-making, financing frameworks, and policy design.   #EnergyAccess #JustTransition #AIforDevelopment #GeospatialIntelligence #DigitalDevelopment #SDG7 #UNDP #IBM #IEA

🌀 From Predictive Models to Agentic AI — in Just a Few Hours I wanted to experience what it’s like to build an agentic pipeline firsthand. So I did. Use case? Predictive maintenance for wind turbines — minimizing downtime and maximizing efficiency. Here’s the flow I created in Dataiku: 🛠️ Agents in Action: Data Collector Agent → pulls live sensor data (temperature, vibration, performance). Data Processor Agent → cleans, formats, and normalizes the inputs. Predictive Model Agent → Deploys ML models to forecast failures (Offshore, Onshore Small, and Onshore large turbines). Maintenance Scheduler Agent → prioritizes turbine maintenance based on predicted risks. The result? A conversational interface powered by Agentic AI — One place. One entry point. One orchestration layer. And it was built in just a few hours, thanks to the reusable descriptive and predictive artifacts I already had in Dataiku. Here’s what I learned: ✅ Agents get complex fast ✅ Visibility, governance, and usability are critical ✅ If you can’t trust or trace your agents, you’re not scaling — you’re gambling 🔍 With Dataiku, building and debugging agents is possible and straightforward. 📣 Curious how this works in your industry? The Dataiku team will be talking about this stuff live, bring your questions https://lnkd.in/gJ-qJi8s #AgenticAI #PredictiveMaintenance #WindEnergy #DataScience #Dataiku #MLops #AIatScale #ConversationalAI