Abstract: A computer-implemented method of projecting a utility load demand including providing vehicle parameters for a plurality of areas and location parameters associated with the plurality of areas to a machine learning (ML) model, iteratively training the ML model to identify relationships between the vehicle parameters, the location parameters, and historical utility data associated with the plurality of areas, receiving a target area and a future target date, providing the target area and the future target date to the trained ML model, obtaining and providing target vehicle parameters and target location parameters for the target area to the trained ML model, determining, via the trained ML model, an EV charging forecast for the target area at the future target date and projecting, via the trained ML model, a utility load demand within the target area at the future target date based on the EV charging forecast.

Abstract: A computer-implemented method of dynamically generating a charger map for electric vehicle (EV) charger locations. The method includes providing one or more parameters for a plurality of target areas to a machine learning (ML) model. The ML model is iteratively trained to identify relationships between the parameters using historical data corresponding to the plurality of target areas. One or more target parameters for a user-specific target area are received along with one or more user-specific weights representing one or more prioritized charging features associated with the user-specific target area. A charger map is generated via the trained ML model for the user-specific target area including one or more locations for EV chargers within the user-specific target area. The charger map is optimized relative to the one or more prioritized charging features.