A Planetary-Scale
Energy Data Engine
Pathian® is not a point solution or a reporting tool. It is a planetary-scale energy data engine designed to standardize how the HVAC and energy industries measure, compare, and optimize performance.
At its core, Pathian converts fragmented building automation and meter data into weather-independent performance curves and scores that can be compared consistently across equipment, buildings, portfolios, and geographies. This standardization is what allows energy performance to scale—from a single air handler to an entire utility territory—without losing accuracy or context.
The Problem: Energy Data Doesn’t Scale
Building automation systems use inconsistent point naming and structures. Energy performance is highly weather-dependent and difficult to normalize. Traditional benchmarking and measurement & verification rely on slow, project-specific regression models. Results cannot be reliably compared across buildings, systems, or programs.
As a result, most energy decisions are made locally, manually, and after the fact.
The Solution: GEES™ — A Global Energy Efficiency Standard
GEES is a standardized, curve-based representation of how energy-consuming systems behave across outdoor air temperature conditions. Instead of relying on static baselines or one-off regressions, Pathian continuously models actual system behavior and normalizes it against weather and operating context.
The output is a consistent, machine-readable performance signature that can be compared across peer systems and facilities, tracked over time, aggregated across portfolios, and used for real-time optimization and verification.
How the Engine Works
Pathian enables OEMs and platform providers to embed standardized,
GEES-based performance intelligence directly into their products.
This creates a common language for measurement, optimization,
and verification across deployed systems.
Native integrations, automated data normalization, and curve-based
analytics reduce the need for site-specific configuration.
OEMs can scale deployments without rebuilding analytics for
every customer or geography.
GEES curves and AOS logic can be integr
What This Enables
Building automation systems use inconsistent point naming and structures. Energy performance is highly weather-dependent and difficult to normalize. Traditional benchmarking and measurement & verification rely on slow, project-specific regression models. Results cannot be reliably compared across buildings, systems, or programs.
As a result, most energy decisions are made locally, manually, and after the fact.
Built for Scale, Delivered Through Microsoft Azure
Azure enables elastic scaling from single buildings to millions of data streams, secure enterprise-grade deployment, marketplace-based delivery and metered billing, and integration into existing utility, OEM, and enterprise ecosystems.
The result is an energy data engine that is not limited by geography, portfolio size, or program design.
One Engine. One Standard. Many Applications.
Once energy performance is standardized, it can be optimized, compared, verified, and automated at a scale the industry has never had before.
That is what Pathian makes possible.