The role of machine learning can be illustrated by its impact on the
power sector transformation: The energy transition will have a profound effect on the power system, including the transmission (high voltage) and distribution (low voltage) grid. Data-driven power grid analysis and optimisation have attracted wide attention in recent years and machine learning has been used in studies of load forecasting, power flow calculation, grid stability and security, integration of distributed generation (renewable energy sources) and optimisation of flexibility assets such as batteries. Based on historical records of consumption, weather data and grid response, and guided by knowledge of the physics of power networks, machine learning algorithms can provide decision support in the design, management and maintenance of power systems and thus improve their economic efficiency and security.