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An increasing embrace of Artificial Intelligence by the electricity sector may lead to a world where power generation, distribution, and transmission operations are automatically optimized, where the grid is balanced independently of any human interventions, where trading and arbitrage decisions are made in nanoseconds at a scale that only machines could tackle, and where end-users never have to worry about searching for a better supplier or changing the temperature manually.

However, the first issue that AI could tackle is the matching of the volatile and difficult to forecast supply and demand, caused by the steady growing share of renewables in the energy mix. Supply is mainly driven by weather and the short -term demand is quite difficult to forecast. An inaccurate load forecast in the power grid can affect many stakeholders: e.g. the power producers, TSOs and the electricity retailers. 

In the future, machine and deep learning technologies could forecast demand and supply in real time and optimize load dispatch, thereby saving energy and cost. More reliable forecasts would allow utilities to delay or even avoid ramping up a fossil-fuel-powered station. It would also offer cost-effective alternatives to operators, who currently consider building new plants to absorb seemingly impossible variability. 

Electric utilities are starting to explore artificial intelligence technologies to produce more accurate short-term load forecasts. DeepMind, the AI startup bought by Google in 2014, is currently working with National Grid to predict supply and demand peaks in the United Kingdom by using weather-related variables and smart meters as exogenous inputs, hoping to cut national energy usage by 10 percent and maximize the use of renewable power despite its intermittence.