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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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Buildings in softwareAnd
Software in buildings
Jason Trager
(A discussion about Physical simulation and Empirical modeling)
Feedback : TinyURL.com/SDBkickoff“FeedForward”: Tweet #SDBKickOff
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?
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? • Building Geometry
Slide credit : Ronxgin Yin, DRRC
Model Calibration
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Subm
eter
ed D
eman
d Po
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/ kW
Comparison between Measured and Simulated Demand Power for Each Submeter Point
Average Demand Power (kW) Simulated Demand Power (kW)
• 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
• 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
What does an empirical model look like?
What does an empirical model look like?
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
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?
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?
Where does the pareto optimum occur with respect to sensor and data density in a building? When does additional data not yield more benefit?
Baseline Measures
Fault Detection
Automated Control
Sensor Augmentation
Production Scale