Load profiling is the process of collecting, structuring, and analyzing time-based electrical demand data to build a load profile for a site, charger network, or customer segment. In EV charging, load profiling is used to understand when and how charging demand occurs, how it overlaps with building loads, and how to design and configure load management for reliable, cost-effective operation.
What Is Load Profiling?
Load profiling turns raw measurement data into an actionable demand pattern by:
– Gathering interval data (e.g., 1-minute, 15-minute, hourly) from meters and chargers
– Cleaning and aligning data (time zones, missing values, abnormal spikes)
– Separating baseline building load from EV charging load (where possible)
– Aggregating demand into daily/weekly patterns and peak periods
– Identifying typical and worst-case scenarios
The result is a structured view of demand behavior that can be used for planning, control, and forecasting.
Why Load Profiling Matters in EV Charging Infrastructure
Load profiling supports smarter decisions across the EV charging lifecycle:
– Determines how many chargers a site can support before electrical upgrades are needed
– Improves configuration of dynamic load management and load balancing
– Reduces risk of breaker trips by understanding peak overlap between loads
– Identifies cost-saving opportunities through off-peak charging and peak shaving
– Supports fleet readiness planning by matching charging windows to operations
For multi-tenant or public sites, load profiling also helps justify infrastructure investment with realistic demand assumptions.
Data Sources Used for Load Profiling
Common data inputs include:
– Utility or facility smart meter interval data (total site kW)
– Switchboard power meters and CT clamp measurements per phase
– EV charger session logs (start time, end time, kWh delivered, power levels)
– Backend analytics from a CPMS (utilization, concurrency, dwell time)
– Operational schedules (shifts, fleet dispatch times, site opening hours)
Combining site meter data with charger data produces the most accurate representation of headroom and constraints.
How Load Profiling Is Performed
A typical load profiling workflow includes:
– Define the profiling goal (capacity planning, tariff optimization, grid compliance)
– Collect representative data across relevant periods (weekdays/weekends, seasons)
– Normalize demand curves to compare days and detect patterns
– Identify peak demand moments and duration of peaks (not just maximum kW)
– Build scenarios (current state, +10 EVs, +50% utilization, new tariffs)
– Translate findings into control rules for load curtailment and load balancing
For fleets, profiling often focuses on overnight clustering and morning departure readiness.
Use Cases of Load Profiling in EV Charging
Load profiling is commonly used for:
– Capacity planning: estimating chargers supported within a site power limit
– Tariff strategy: shifting charging away from expensive peak periods
– Electrical design: sizing feeders, switchgear, and phase distribution
– Operational policy: setting priority rules (fleet vs public, staff vs visitors)
– Performance monitoring: detecting abnormal load behavior or degradation in charging power
– Demand response: verifying how much load can be curtailed when requested
Key Metrics in Load Profiling
Typical metrics analyzed include:
– Peak demand (kW) and peak duration
– Average demand and daily energy (kWh)
– Concurrency of charging sessions (how many EVs charge at once)
– Load factor (how “flat” the demand curve is)
– Time-of-day and day-of-week patterns
– Phase loading balance in three-phase sites
These metrics help convert raw demand into actionable control settings.
Practical Considerations and Limitations
Accurate load profiling depends on data quality and context:
– Short profiling windows can miss seasonal peaks (heating/cooling impact)
– Missing or misconfigured meters lead to incorrect headroom calculations
– New EV adoption can change patterns quickly, requiring periodic re-profiling
– Charger data alone may hide building peaks that cause overload risk
– Control decisions must respect minimum charging currents and user experience
Load profiling is most effective when updated over time and linked to real operational behavior.
Related Glossary Terms
Load profile
Load measurement
Load management
Dynamic load management
Load balancing
Load curtailment
Site power limit
Peak shaving
Demand response
Charger utilization