Local forecasts of electrc vehicles for grid planning purposes

Paper i proceeding, 2022

Electrification of passenger vehicles is rapidly becoming the main alternative for decarbonizing transportation. The high power associated with charging of electric vehicles is likely to require actions from grid operators. Using machine learning and GIS analysis we produce forecasts of electric vehicles in very small cells, down to a few hundred meters for Norway. Using a baseline comparison, we find that a random forest model produces the overall lowest error, with a Mean Absolute Error of 14.0, and Mean Absolute Percentage Error of 33.9%. We find that both the existing vehicle fleet, and forecast shows that there is a large variation in electric vehicle adoption between cells. With knowledge where and when electric vehicles are adopted, grid operators can better plan their future investments related to electric vehicle charging, and thereby reduce investment costs.