Grid operators and charge-point operators need to know, hours or days ahead, how much power an EV charging depot will draw. Under-forecast and stations brown out or throttle charging speed; over-forecast and expensive grid capacity sits idle. This scene shows a simplified depot of charging bays next to a live 24-hour load curve, with two families of machine-learning model trying to predict it: gradient-boosted trees (XGBoost) and a recurrent LSTM network.
Real charge-point operators often ensemble several models — combining tree-based learners that handle categorical context (holidays, weather bins) well with sequence models that capture momentum — because no single architecture wins on every horizon.
A 3D EV charging depot sits beside a live 24-hour load chart, where gradient-boosted trees (XGBoost) and an LSTM network each try to predict the electricity the depot will draw — and the chargers themselves glow green-to-red as each model's forecast drifts from reality.
XGBoost produces a stair-stepped, hour-by-hour forecast typical of tree-based models trained on tabular features, while the LSTM produces a smoother but slightly lagged forecast typical of sequence models reasoning over recent history.
Scrub time of day or let it autoplay, switch between weekday commute and weekend leisure charging patterns, dial ambient temperature to trigger preconditioning-driven demand spikes, and pick which model's forecast drives the depot's error colouring.
Utilities use exactly this kind of short-term load forecast to decide how much spare grid capacity to hold in reserve for EV charging — get it wrong too often and either drivers get throttled or capacity sits expensively idle.