Think of this as giving power plants and grids a smart brain that constantly watches operations, predicts future demand and equipment issues, and suggests optimal ways to run everything more safely and cheaply.
The paper describes how AI in energy is used to optimize power generation and distribution, forecast demand, improve maintenance of equipment, and integrate renewables—addressing high operational costs, unplanned outages, and reliability challenges in power systems.
Proprietary operational and sensor data from generation, transmission, and distribution assets can become a defensible moat, as models trained on this data improve forecasting, optimization, and maintenance performance over time.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Data Quality
Early Majority
The core differentiator for utilities and energy firms is not the generic AI techniques, which are widely available, but the depth of integration with grid operations and the quality and breadth of historical operational data feeding forecasting and optimization models.