Artificial intelligence (AI) has made its way into the energy industry, offering the potential to optimize the use of distributed energy resources (DER), electric vehicles, and the Internet of Things (IoT), while aligning with the current pace of change that utilities, regulators, and customers have come to expect.

Use Cases

Smart meters

Smart meters help customers tailor their energy consumption and reduce costs. Greater usage would also open up a massive source of data, which could pave the way for more customized tariffs and more efficient supply.

Predicting spikes

Artificial intelligence systems can learn from experience, which means that they can analyze past patterns in data, and use them to predict the future, and therefore manage better. This is particularly useful in managing power grids, because the system can learn when spikes in demand are more likely to occur, and enable better use of the power at times of lower demand. Artificial intelligence systems can be used to propose variable pricing models to encourage use at low-demand times.

More efficient grid operation and storage

Balance the grid. Digitally enabled demand-side management solutions such as reflective pricing tariffs, energy storage and home energy management solutions can help balance the grid and reduce long term infrastructure investment.

Predictive maintenance

Companies today use machine learning in maintenance and support services. By means of sensors, artificial intelligence helps capture the energy consumption of individual machines, analyze maintenance cycles, and then optimize them in the following stage. Operating data indicates when a part must be replaced or where there is likely to be a defect. As the amount of data increases, the system becomes better at optimizing itself and making more accurate predictions.


  • More efficient and cost-effective supply and usage of energy
  • Optimized power management
  • Time saving
  • More efficient and consistent renewable energy supply in areas such as improved prediction and optimization of wind power