Charging Optimization

AI forecasts charging demand and availability to help planners choose routes, charging stops, and charging schedules.

Business impact

• Time — Pairpoint optimizes routes and charging stops for time.
• Cost — Pairpoint optimizes routes and charging stops for cost.
• Operational efficiency — SERVEO uses forecasting to improve charging coordination and efficiency.
• Operational costs — GREEN.DAT.AI states the forecasting service reduces operational costs.

Data requirements

• Historical energy consumption patterns (tabular) — GREEN.DAT.AI uses historical charging-station data as input.
• Location-specific information (tabular) — Latitude and longitude are used as input features for forecasting.
• Forecast targets for demand or charging conditions (tabular) — The systems predict future energy demand or charging-related conditions.

AI methods and techniques

• Forecasting — Used to predict future charging demand or conditions for planning routes and schedules.
• Machine learning — Used to generate the forecasts that drive charging optimization.
• Deep learning — Used in the EDCF service to detect patterns in historical charging-station data.

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: Pairpoint, SERVEO.