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Facts & Figure
95.95%
Availability
Our APIs can be used for
mission-critical systems.
25 Million
APIs Call Daily
Our APIs define the
Industry's standards.
1 Million
Active User
Millions of people trust us.
About us
Building Energy Modeling analytics provides the ability to automatically identify the energy consumption model (a.k.a. 'Thermal signature’) of a Building. It can consider several drivers (e.g. the production for a plant, the number of people...) that also influence the energy consumption.
Building Energy Modeling analytics includes four features:
createModel- Identify the best daily, weekly or monthly energy model of a building among reference model types (ASHRAE), with automatic time binning and outlier detection (createModel).
Apply an existing model to new data to estimate the energy that should have been consumed in these conditions. This can be used to detect anomalous consumptions or track progress of energy conservation measures during a retrofit operation (applyModel).
- Get metrics to ensure a model is good enough to compute savings (assessModelQualityForSavings)
- Get information on an existing model(getModelInformation
APIs Product
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Building Energy Modeling API
Building Energy Modeling API. This API automatically identifies the best thermal model for a building, a plant in order to assess energy savings, to detect abnormal energy consumption.
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Energy Modeling API-Desc
The Building Energy Modeling API is a service that enables the calculation and analysis of energy consumption in buildings for efficiency and sustainability assessment.
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Power Flow Pro
A Power Flow Pro API enables real-time monitoring and analysis of electrical power distribution networks, providing critical insights for optimized energy management and reliability.
Assess model quality for savings
You may wish to use a model created with this component to measure future energy savings, for example after some retrofit operation. But you do not need the same level of quality from your model to measure 2% savings or 30% savings: a much more precise prediction is needed to measure small savings of 2% and be sure that this is not 'just noise'. Also, the shorter the assessment period will be, and the more precise you model needs to be. This service helps you to determine if your model is good enough to measure a given target of energy savings with a given confidence level, based on predictions made during a given number of days (assessment period).
POST /v1/assess-model-quality-for-savings
{
"Inputs":
{
"modelID": [{
"modelID": "your_model_id_here
}]},
"GlobalParameters": {
"confidenceLevel": "0.05",
"uncertaintyRatio": "0.1",
"targetSavingsRatio": "0.3",
"assesmentPeriod": "100"
}
}
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