Buildings in software and software in buildings
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Buildings in software And Software in buildings . (A discussion about Physical simulation and Empirical modeling). Jason Trager. “ FeedForward ”: Tweet # SDBKickOff. Feedback : TinyURL.com / SDBkickoff. Why do we make Building energy models? . To estimate energy usage in a new building

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Buildings in software and software in buildings

Buildings in softwareAndSoftware in buildings

(A discussion about Physical simulation and Empirical modeling)

Jason Trager

“FeedForward”: Tweet #SDBKickOff

Feedback : TinyURL.com/SDBkickoff


Why do we make building energy models

Why do we make Building energy models?

  • To estimate energy usage in a new building

  • To evaluate efficacy of a retrofit

  • To explore a theoretical design

  • To match the building code

  • MPC – Reduced order model


How good are these models

How good are these models?


Retrofit using b e modeling

Retrofit using B.E. modeling

  • Model building

  • Simulate changes

  • Initiate changes

  • Calibrate model

  • Re-simulate

  • Change more building settings

  • Recalibrate


What does a model look like

What does a model look like?

  • Building Geometry

Slide credit : Ronxgin Yin, DRRC


Model calibration

Model Calibration


Buildings in software and software in buildings

  • Does it make sense to use sensors to create better building models? How is this different than information that could be gleaned from sensors alone?


Empirical building adjustment

Empirical building adjustment

  • Analyze data from sources

  • Make intelligent choices about adjusting building settings

  • Measure results

  • Produce counterfactual from data

  • Compare actual to predicted

  • Make more adjustments


Actual dr event

Actual DR Event


What does an empirical model look like

What does an empirical model look like?


What does an empirical model look like1

What does an empirical model look like?


How good is empirical analysis

How good is empirical analysis?

MAE: Mean Absolute Error

RMSE: Root Mean Squared Error

MAPE : Mean Absolute Percent Error

RMSPE: Root Mean Squared Percent Error

RelBias: Relative Bias


Empirical modeling and software control

empirical modeling and software control

  • Measure, predict responses of sensor streams

  • Search for faults

  • Search for mis-labeled streams

  • Institute rule based control?

  • Apply machine learning?

  • Apply Model Predictive Control?


Buildings in software and software in buildings

How do we use data richness to develop better quantative ways to control the building?

How will this succeed over the need for simulating the building and adjusting it manually?


Buildings in software and software in buildings

Where does the pareto optimum occur with respect to sensor and data density in a building? When does additional data not yield more benefit?


Buildings in software and software in buildings

Baseline Measures

Fault Detection

Sensor Augmentation

Automated Control

Production Scale


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