Transform Energy management with AI solutions
AI for Energy
Artificial Intelligence makes energy industry more efficient and secure by analysing and evaluating the data volumes.
AI impact and potential in reshaping the future design of energy system are becoming more and more evident.
Typical areas of application are electricity trading, smart grids, or the sector coupling of electricity, heat and transport.
Prerequisites for a cutting-edge application of AI in the energy system are the digitalisation of the energy sector and the availability of a correspondent evaluable large set of data.
Applications
Non-intrusive Load Monitoring
Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics, find anomalies and save energy.
Anomaly detection
It detects data points that deviate significantly from expected behavior and shows only relevant anomalies, cutting noise and cognitive load. It works best with high-quality data where at least one measurement contains enough information to reveal the anomaly—something a skilled analyst could find given time. It also leverages time as a key variable, since temporal patterns are often essential to define normal system behavior.
AI in electricity trading
With AI, large datasets relevant to electricity trading—such as weather and historical data—can be evaluated systematically, enabling more accurate forecasts that improve grid stability and security of supply. Continued innovation in forecasting is key to accelerating renewable integration, with machine learning and neural networks playing a central role. In recent years, AI has already improved forecast quality, contributing to a reduced need for reserve control even as the share of volatile generation has increased.
Transform Energy management with AI solutions
Modern power systems are becoming increasingly decentralised, digital and complex. With thousands of distributed units producing, consuming or storing electricity, maintaining balance across the grid now requires continuous, data-driven coordination and far more intelligence than traditional methods can provide.
Virtual Power Plants (VPPs) link distributed energy resources—such as solar, wind, hydro, storage units, CHP systems, or flexible loads—into a single coordinated network. Even small assets can act collectively as a large, controllable entity capable of providing flexibility and participating in advanced market mechanisms.
AI enhances grid reliability by detecting anomalies in generation, consumption and transmission, providing alerts before issues escalate. It also supports predictive maintenance, identifying optimal intervention times and reducing both operational disruptions and costs.