MACHINE LEARNING
“ EVs are critical for reducing fossil fuel reliance , but by 2030 , EV charging capacity will need to be 12 times greater than it is today ,” Rob McInerney , CTO at Lightstate , tells AI Magazine . “ Emerging AI solutions that can forecast localised EV demand and enable complex , real-time decision-making will be a huge weapon in slowing down climate change .”
Demand for energy will also change , and a larger portion of that energy will come from renewable sources . For example , if a low-wind summer results in minimal power generation from a wind turbine , employing a machine learning model to optimise operations can enhance energy security .
ML models can also allow better placement of future renewable assets . Through geospatial decision-making , it uses data from advanced mapping technologies to gather and analyse spatial information from sources such as satellites and ground observations to identify optimal sites for renewable energy assets .
“ Developers can assess critical factors such as sun exposure , wind patterns and water resources , which are essential for determining the feasibility and potential energy yield of a project ,” Rob explains .
104 July 2024