Improving Forecasting Accuracy for Energy Operators

AI-based modelling is becoming a practical tool for managing distributed energy networks. The report "Ask the Energy System: AI Assisted Energy Modelling" shows how a combination of machine learning, agent-based models and open data supports real-world low-voltage network planning. Key findings: • The growth of decentralised resources (DER, EVs, batteries) increases pressure on local networks, while current tools often lack the required resolution • Agent-based modelling helps reproduce interactions between local network elements and assess the impact of new connections on capacity and stability • Machine learning models forecast load and generation in 5-minute intervals with higher accuracy than classical statistical methods • LLM integration improves handling of incomplete or inconsistent data and enables interactive scenario analysis • Use of open time-series repositories and weather APIs improves reproducibility and independent validation of results • Open-source architectures enhance compatibility, transparency and reduce the cost of integrating new data sources and forecasting modules • Main application areas include network capacity assessment, EV charging planning and energy-storage siting The report concludes that building flexible and resilient energy systems depends on compatible and verifiable tools that combine data, models and engineering context within a single analytical environment. What limits wider use of AI in energy modelling? #EnergySystems #AIinEnergy #DataModelling #EnergyTransition #MachineLearning #SmartGrid #OpenSource #GridForecasting #EnergyAnalytics

AI is taking the energy industry to the next level, and utilities are taking notice. Leading utilities like Avista, Pacific Gas and Electric Company, and Ameren are now using #AI and #machinelearning to optimize complexities across the power system. The volatility of extreme weather, evolving market dynamics, and the challenge of renewable integration demand a new approach that legacy models can’t support. At Amperon, we’ve been building for this transition since 2018. When I spoke with Herman Trabish, I shared how we use AI/ML algorithms to retrain our models hourly, process 40K+ granular weather points, and deliver forecasts that are 2-3x more accurate than the ISO—all while getting smarter and faster with each iteration. Utilities that invest in AI aren’t just adapting; they’re future-proofing. Read more in Utility Dive.

The grid is failing to keep up with renewables. Wind and solar are flooding the system. Traditional forecasting models look at historical data. But extreme weather and distributed generation have made those models obsolete. Sean Kelly, CEO of Amperon, walked me through how his company is solving this with AI-driven forecasting. Their platform tells grid operators, battery owners, and energy traders exactly when supply and demand will spike or crash. The problem: when solar goes down at 7 pm and wind drops with it, families are still home. Offices are still running. Demand is high and supply is vanishing. The solution: real-time machine learning that adapts as conditions change. Amperon's platform shows battery operators the exact hours to discharge. It tells data centers when to back down their load. It helps grid operators avoid blackouts. Sean put it simply: "Forecasting is the operating system of the modern grid." If you're working in energy trading, storage, or grid operations, this conversation will clarify how AI is changing the game. Tune in to the full episode. https://lnkd.in/gWqwR5be #RenewableEnergy #AIinEnergy #GridReliability

𝗚𝗿𝗲𝗲𝗻 𝗘𝗻𝗲𝗿𝗴𝘆 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗔𝘇𝘂𝗿𝗲 𝗔𝗜 There are so many stories right now about leveraging new Technology in the renewable sector especially with AI. I came across how Enerjisa Üretim is Powering Turkey with Azure AI and thought I would share As Turkey’s largest independent power producer, Enerjisa Üretim is at the forefront of the renewable energy revolution. Managing 21 power plants and balancing fluctuating energy demand required a scalable, AI-driven solution. The Challenge: • Complex energy forecasting for hydro, wind, and solar plants • Fluctuating demand and unpredictable renewable energy generation • A need for real-time, AI-powered decision-making The Solution: By adopting Microsoft Azure AI and machine learning, Enerjisa Üretim built an advanced energy forecasting model that predicts energy production more accurately, optimizing power distribution. The Impact: - 80% improvement in energy demand forecasting accuracy - Increased operational efficiency for sustainable energy production - Optimized resource management, reducing waste The result? A more stable, efficient, and sustainable energy grid that helps power Turkey’s transition to greener energy. The future of energy isn’t just renewable—it’s data-driven. How is your organization leveraging AI for efficiency?