Energy consumption analytics is the process of measuring, analyzing, and interpreting how much electricity is used, when it is used, and what drives that usage. In EV charging, it turns charging-session data and site-meter readings into insights that help optimize cost, capacity, charging performance, and sustainability reporting across chargers, depots, and workplaces.
What Is Energy Consumption Analytics?
Energy consumption analytics focuses specifically on energy use (kWh) and related patterns, often combining EVSE data with building and grid context.
– Charger-level data: kWh delivered, session duration, peak kW, idle time, start/stop times
– Site-level data: total facility consumption, feeder loads, transformer utilization, sub-meter readings
– Cost data: tariffs, time-of-use pricing, demand charges, cost centers
– Sustainability data: emission factors, carbon intensity, renewable attribution
The result is a clear picture of consumption behavior and its financial and operational impact.
Why Energy Consumption Analytics Matters for EV Charging
EV charging can quickly become one of the largest and most variable electrical loads on a site.
– Identifies where energy is going across chargers, users, vehicle groups, and time periods
– Reveals peak demand drivers to support load management and reduce demand charges
– Improves fleet readiness by matching energy delivery to shift schedules and operational needs
– Supports expansion planning with evidence-based sizing of chargers and electrical capacity
– Enables accurate internal billing, reimbursement, and cost allocation
– Provides auditable inputs for CO₂e reporting and sustainability dashboards
Key Metrics Tracked in EV Charging Consumption Analytics
– Total kWh per day/week/month per charger, site, or fleet group
– kWh per session and kWh per vehicle/driver
– Peak demand (kW) and the exact time peaks occur
– Load profile: hourly consumption curves and seasonal patterns
– Utilization vs consumption: occupancy compared to energy delivered
– Idle energy and overhead (where measured upstream vs delivered kWh)
– Aborted / partial sessions and their energy impact
– Carbon metrics: kg CO₂e based on emission factors and reporting method
How Energy Consumption Analytics Works
Energy consumption analytics typically integrates and normalizes multiple data sources.
– Session and metering data from chargers via OCPP into a CPMS
– Billing-grade data from MID meters or site submeters for accurate accounting
– Facility energy data from building meters or an energy management system
– Tariff tables (TOU periods, demand charge rules, contracted capacity limits)
– Optional DER data: PV generation, battery charge/discharge, import/export flows
Analytics software then aggregates the data into dashboards, alerts, and optimization recommendations.
Common Use Cases
– Workplace charging: understand adoption, fairness, and when to add more points
– Fleet depots: ensure vehicles receive required energy before dispatch, reduce peak loads
– Public destination sites: evaluate demand patterns and optimize pricing and bay turnover
– Cost allocation: charge employees, departments, tenants, or contractors accurately
– Energy optimization: schedule charging to off-peak periods and align with onsite solar
– Performance troubleshooting: detect unusually low energy delivery that signals faults or power throttling
Optimization Actions Enabled by Consumption Analytics
– Implement or tune dynamic load balancing to limit site peaks
– Shift charging to lower-cost windows using scheduled charging or smart policies
– Identify underutilized chargers and relocate or reconfigure bay allocation
– Plan staged upgrades: add connectors, uprate feeders, or add storage based on measured demand
– Reduce idle time with workplace rules or pricing signals (where applicable)
– Improve sustainability reporting accuracy by applying the correct emission factors per location
Limitations to Consider
– Data quality depends on connectivity, correct configuration, and metering accuracy
– Delivered kWh at the charger may differ from upstream kWh due to losses and auxiliary loads
– Privacy controls are needed when analytics is tied to employee identity or driver accounts
– Tariff complexity can make cost analytics inaccurate without correct utility billing rules
– Cross-site benchmarking requires consistent units, time zones, and data structures
Related Glossary Terms
Energy Analytics
Charge Point Management System (CPMS)
OCPP
MID Metering
Load Management
Dynamic Load Balancing
Demand Charges
Emission Factors