iMTED AI Energy Assistant

At iMTED, we are developing an AI Energy Assistant that identifies inefficiencies and savings potential in commercial buildings and facilities by analyzing existing smart meter data, including heating, cooling, electricity, and water consumption. Using physics-informed neural networks, it provides short- and long-term load forecasting, real-time detection of anomalies such as sensor faults, equipment malfunctions, and leaks, as well as dynamic, price-based energy procurement and one-click reporting. By transforming raw data into actionable insights, iMTED enables energy intensive SMEs and public institutions to reduce energy costs and associated emissions by up to 20%, reporting effort by up to 90% while minimizing operational complexity. The solution integrates seamlessly into existing processes and remains adaptable to evolving energy systems such as PV, energy storage, and other renewable technologies.