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$4.0MLarge Retail Chain: Energy Optimization Across 600+ Locations

A leading retail chain with a national footprint and active corporate sustainability goals deployed DemandQ's Intelligent Demand Optimization platform across its entire portfolio, achieving $4 million in verified savings with zero capital expenditure.

$4.0M
Total Verified Savings: $2.8M
Demand Savings: $1.2M
Energy Savings: 175,000 kW
Peak Demand Mitigated: 7,800 MT
CO₂ Reduced: 10%
Avg Demand Reduction

Overview

This national retail chain operates hundreds of locations across the United States, each running multiple rooftop HVAC units throughout the business day. With active corporate sustainability commitments and significant energy spend, leadership sought a demand management solution that would deliver measurable savings without touching occupant comfort or requiring capital investment.

DemandQ's Intelligent Demand Optimization platform was deployed across the full portfolio, leveraging the existing building automation system as the integration layer: no new hardware, no infrastructure upgrades, and no disruption to day-to-day operations.

The Challenge

Uncontrolled Peaks Across a National Portfolio

01
Demand charges represented approximately 40% of total monthly utility spend across the portfolio, the single largest controllable cost driver.
02
During warm weather months, dozens of HVAC units across each location would restart simultaneously following a thermostat setback or system recovery event, creating sharp coincident demand peaks recorded by the utility meter.
03
No active demand management program was in place. Demand peaks were uncontrolled and unmonitored at the site level.
04
The scale of the portfolio (600+ locations spread across multiple utility territories) required a centralized, software-driven approach rather than site-by-site interventions.
05
Corporate sustainability goals required verifiable CO₂ reduction data that could be included in annual reporting.

The Solution

Software-Only, Portfolio-Wide Deployment

DemandQ deployed its patented Intelligent Demand Optimization platform across the retailer's full portfolio via a software-only integration with the existing building automation infrastructure. No capital expenditure was required.

Predictive Demand Analytics

DemandQ's predictive forecasting model anticipates demand peaks before they form, enabling proactive load staggering rather than reactive curtailment.

Patented Queuing Technology

Rather than turning off equipment, DemandQ's queuing system intelligently staggers the restart sequence of HVAC units, eliminating the coincident peak without cycling any unit off during occupied hours.

Comfort Safeguards

Every site was configured with temperature deviation limits. No zone was allowed to drift more than one degree Fahrenheit from setpoint during an optimization event.

Centralized Dashboard

Facilities and energy management teams monitored real-time performance across all 600+ locations from a single operations portal, with site-level and portfolio-level reporting.

Verified Results

All results were measured and verified using IPMVP Option C methodology, whole-facility measurement with regression analysis to isolate DemandQ's contribution from weather, occupancy, and operational variables.

Total Verified Savings: $4.0M
Demand Charge Savings: $2.8M
Energy Consumption Savings: $1.2M
Peak Demand Mitigated: 175,000 kW
CO₂ Emissions Reduced: 7,800 MT
Average Demand Reduction: 10%

Measurement & Verification Methodology

Savings were verified using two complementary testing protocols, both aligned with IPMVP Option C standards:

Two-Month Inactive / Active Study

A full calendar month with DemandQ inactive (baseline period) was immediately followed by a full calendar month with DemandQ active (performance period). Both months were normalized for weather and occupancy to produce an apples-to-apples savings calculation.

Day On / Day Off Testing

Within the same billing period, alternating days were designated as DemandQ-active and DemandQ-inactive. This intra-period comparison controls for seasonal variation and provides high-confidence savings attribution at the site level.

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