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🔌 How Machine Learning Forecasts Electric Vehicle Charging Demand

An interactive 3D depot of EV chargers next to a live 24-hour load chart, showing how gradient-boosted trees (XGBoost) and LSTM networks forecast electricity demand — and how well each tracks reality as conditions change.

AI & Machine Learning3DAdvanced60 FPS

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.

🔬 What It Demonstrates

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.

🎮 How to Use

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.

💡 Did You Know?

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.

⚙ Under the hood

machine learningartificial intelligencedemand forecastingelectric vehicleslstm networksxgboostThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install