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Buildings in software And Software in buildings Jason Trager (A discussion about Physical simulation and Empirical modeling) Feedback : TinyURL.com/SDBkickoff eedForward”: Tweet #SDBKickOff

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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 - PowerPoint PPT Presentation

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Page 1: Buildings in software And Software  in buildings

Buildings in softwareAnd

Software in buildings

Jason Trager

(A discussion about Physical simulation and Empirical modeling)

Feedback : TinyURL.com/SDBkickoff“FeedForward”: Tweet #SDBKickOff

Page 2: Buildings in software And Software  in buildings

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

Page 3: Buildings in software And Software  in buildings

How good are these models?

Page 4: Buildings in software And Software  in buildings

Retrofit using B.E. modeling

• Model building• Simulate changes• Initiate changes• Calibrate model• Re-simulate• Change more building settings• Recalibrate

Page 5: Buildings in software And Software  in buildings

What does a model look like? • Building Geometry

Slide credit : Ronxgin Yin, DRRC

Page 6: Buildings in software And Software  in buildings

Model Calibration

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Subm

eter

ed D

eman

d Po

wer

/ kW

Comparison between Measured and Simulated Demand Power for Each Submeter Point

Average Demand Power (kW) Simulated Demand Power (kW)

Page 7: 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?

Page 8: Buildings in software And Software  in buildings

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

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Actual DR Event

Page 10: Buildings in software And Software  in buildings

What does an empirical model look like?

Page 11: Buildings in software And Software  in buildings

What does an empirical model look like?

Page 12: Buildings in software And Software  in buildings

How good is empirical analysis?

MAPE : Mean Absolute Percent Error

MAE: Mean Absolute ErrorRMSE: Root Mean Squared Error

RMSPE: Root Mean Squared Percent ErrorRelBias: Relative Bias

Page 13: Buildings in software And Software  in buildings

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?

Page 14: 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?

Page 15: 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?

Page 16: Buildings in software And Software  in buildings

Baseline Measures

Fault Detection

Automated Control

Sensor Augmentation

Production Scale