AI-powered cooling performance intelligence. CoolingIQ connects energy, load and operating signals to help interpret deviations and support confident technical decisions.
ASYMMETRIC DYNAMICS / COOLINGIQ
In this example, cooling load during four low-demand hours was about 40% above the average for the same hours on the previous three Fridays. The dashboard also shows how the building compares with similar properties and flags low chilled-water ΔT for review.
Review the shutdown schedule, equipment operating status, and whether the cooling valve closes fully.
View full-size dashboardCooling performance is shaped by changing demand, environment and operating conditions. CoolingIQ helps turn complex signals into a clearer picture of performance and where focused attention could add value.
Cooling intelligence / AD-CNot every cooling inefficiency begins with a sudden change. Some emerge unexpectedly; others remain hidden in everyday operation. CoolingIQ brings both into focus.
CoolingIQ recognizes unexpected changes in an individual unit’s cooling performance as they emerge, helping teams investigate abnormal energy use before its impact becomes apparent on the next utility bill.
A unit can operate consistently while using more cooling energy than comparable units. CoolingIQ highlights sustained performance gaps that may indicate underlying issues such as insulation, controls, equipment condition or operating practices—without assuming a specific cause.
A chilled-water cooling system may continue meeting demand while circulating more water than expected. CoolingIQ brings attention to sustained low supply-to-return temperature differences and excessive flow patterns that could signal hidden system inefficiency.
Interpret chilled-water circulation and supply-to-return temperature differences in comparable operating conditions.
Highlight persistent low-ΔT patterns that deserve technical review, even when cooling service appears uninterrupted.
Support follow-up after investigation by checking whether performance returns to its expected operating range.
Low-ΔT detection identifies a potential efficiency concern, not a confirmed root cause. It is relevant where suitable chilled-water flow and temperature data are available.
From energy behaviour to operational exceptions, CoolingIQ frames system information around the decisions people need to make.
Bring HVAC cooling energy, load and operating signals into a coherent view of cooling-system behaviour.
Surface unusual cooling performance, changing patterns and conditions that warrant technical attention.
Make thermal, flow and system behaviour easier to interpret within real operating conditions.
Highlight data gaps, metering inconsistencies and information that may require verification.
Organize observations into understandable priorities and suggested paths for investigation.
Support follow-up reviews to assess whether an intervention produced the intended operational improvement.
Understand cooling behaviour through a wider operational lens, giving emerging deviations and possible causes the context they deserve.
Connect cooling and operating information.
Surface performance changes and data-quality issues.
Guide structured technical review and prioritization.
Examine observed outcomes after operational changes.
Depending on the environment, relevant information may include cooling energy, flow, supply and return temperatures, operating context and other available system signals.
Low Delta T Syndrome refers to an unusually small supply-to-return chilled-water temperature difference. When paired with higher-than-expected water circulation, it may signal an HVAC cooling efficiency issue. CoolingIQ can highlight persistent patterns for investigation; it does not establish the precise cause.
CoolingIQ is designed to accompany potential inefficiency assessments with the supporting observations, an estimated likelihood and suggested actions for technical review. The evidence informs an investigation rather than conclusively identifying a faulty component.
No. CoolingIQ is designed to support engineering and operational decisions with clearer context, priorities and evidence.
CoolingIQ includes an outcome-oriented review path for examining whether observed performance changed following investigation or action.
CoolingIQ places cooling energy, load and operating changes in context so that deviations, data-quality concerns and priorities for technical review can be understood more clearly.
With appropriate data access, CoolingIQ intelligence can be delivered through API integration and displayed in existing smart-building platforms or dashboards. The integration scope is defined for each environment.
Explore how better visibility and intelligence can support the decisions behind your cooling systems.
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