Load Profile

The interval at which demand data is sampled, directly impacting the visibility of transient spikes. Smart meters typically record at 15-minute or 60-minute intervals, while sub-metering can capture second-by-second fluctuations. Higher granularity reveals inrush currents and short-cycle loads invisible to hourly averages.

The ratio of average load to peak load over a specific period, indicating how consistently a facility consumes power. A load factor near 1.0 suggests a flat, efficient profile (e.g., data centers), while a factor near 0.2 indicates spiky, peaking behavior (e.g., stadiums). This metric is critical for peak shaving ROI calculations.

The speed at which demand changes between intervals. High ramp rates (steep cliffs in the profile) stress transformers and require fast-responding assets like batteries. Analyzing standard deviation and max delta between intervals helps classify a profile as stable baseload or highly intermittent, dictating the required response speed of automated demand response (ADR) systems.

Recurring patterns driven by time-of-day, day-of-week, and weather sensitivity. Fourier analysis or autocorrelation functions decompose a profile into its constituent cycles. Distinguishing between diurnal cycles (solar-driven) and weekly cycles (occupancy-driven) is essential for selecting the correct Customer Baseline Load (CBL) calculation method, such as High X of Y averaging.

The probability that an individual asset's peak demand aligns with the overall system peak. A high coincidence factor makes a load an ideal target for Critical Peak Pricing (CPP) events. Analyzing this requires overlaying the individual load profile against the Locational Marginal Price (LMP) curve or system net load to quantify the value of load reduction at specific hours.

The decomposition of total demand into non-flexible baseload (safety lighting, critical servers) and discretionary load (HVAC, pool pumps, EV charging). Load disaggregation algorithms analyze the profile's variance floor to identify the minimum constant draw. The discretionary portion defines the maximum demand response capacity available for bidding into ancillary service markets.

A statistical calculation of what a customer's energy consumption would have been in the absence of a demand response event. The CBL is derived from historical load profile data, typically using a rolling average of recent, similar days. It is the critical reference point for Measurement and Verification (M&V), determining the financial compensation for load reduction.

The strategic reduction of power consumption during periods of highest grid demand to avoid capacity charges and mitigate the need for peaker plant activation. This is achieved by analyzing the load profile to predict peak timing and dispatching behind-the-meter assets like batteries to offset the building's draw from the grid.

The process of rescheduling energy consumption from peak demand periods to off-peak periods without necessarily reducing total energy usage. Unlike peak shaving, load shifting maintains total throughput. A classic example is pre-cooling a commercial building in the early morning (visible as a morning spike in the load profile) to reduce HVAC load during the afternoon peak.

Non-intrusive algorithms that decompose a building's total energy signal into individual appliance loads. By analyzing high-resolution load profile data, these systems identify unique electrical 'signatures'—such as the distinct startup transient of a compressor—to isolate HVAC, lighting, and plug loads without installing sub-meters on every circuit.

A static electricity pricing scheme that defines different fixed rates for specific blocks of time, generally charging higher prices during peak demand hours. TOU rates are designed to reshape the aggregate load profile by creating a persistent economic incentive for consumers to shift consumption away from the system peak.

The ability of an energy-consuming device to modulate its power draw in response to an external signal without compromising its primary operational function. A flexible load profile is not static; it represents a dynamic envelope of possible consumption patterns. Examples include varying the charging rate of an EV or dimming non-critical lighting.