More advanced than industry standards, Bractlet's energy analytics enables fast, exact and cost-effective equipment-level energy modeling.

Bractlet analysis follows three steps—building baseline energy models, identifying savings opportunities and forecasting energy savings.

Building baseline energy models.

We generate dynamic, physics-based building models that enable us to forecast the energy consumption effects of any modifications to a building with unmatched (98%) accuracy. By simulating millions of variables and interactions we can understand relationships between equipment-performance, building occupancy/operations, and outdoor environmental variables.

 
Building model with individual thermal zones generated for analysis. 

Building model with individual thermal zones generated for analysis. 

 

In addition to using cloud-based computing resources, our process is built to limit assumptions and increase speed and accuracy by:

Picture7.png

Using as-designed equipment, architectural information and standard auditing processes.

We use advanced statistical analysis to understand relationships in the building.

We use advanced statistical analysis to understand relationships in the building.

Extracting operational characteristics through automated mining algorithms from BAS and equipment-level energy data.

Low error from simulated vs monitored power display accuracy of energy model. 

Low error from simulated vs monitored power display accuracy of energy model. 

Automatically calibrating energy simulations using both equipment and building consumption data.

Identify energy savings.

Once a baseline energy model is established, Bractlet uses in-house energy experts and automated analytics—based on our historical database—to identify areas of savings.

Our unique understanding of a building allows us to:

  • Pinpoint non-capital energy conservation measures.
  • Identify savings from complex and deep energy retrofits.
  • Consider combined energy efficiency, renewable generation and energy storage solutions.
Understand and predict effects of savings measures at the equipment-level.

Understand and predict effects of savings measures at the equipment-level.

Predict energy savings.

Bractlet simulates possible energy conservation measures by manipulating the baseline energy model. Our modeling was designed to consider interdependencies between savings measures and building systems.

Our granular simulations allows us to run proprietary uncertainty analyses that quantify the risk of achieving energy savings, providing decision makers with key insights on energy efficiency opportunities. 

Savings distributions allow for risk and uncertainty to be quantified

Savings distributions allow for risk and uncertainty to be quantified