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Information-Based Building Energy Management SEEDM Breakout Session #4.

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Presentation on theme: "Information-Based Building Energy Management SEEDM Breakout Session #4."— Presentation transcript:

1 Information-Based Building Energy Management SEEDM Breakout Session #4

2 Outline Example future capabilities Observe: data collection Analyze: data analysis Act: decisions and actions Test bed for smart building research 2

3 Example Future Capabilities Visualize energy usage  Breakdown energy usage for a building  Instead of a single energy bill with an aggregate reading  Compare energy usages across similar buildings  This building is at 90% percentile energy efficiency  Detect abnormal energy usage Context-aware energy analysis, suggestions, actions  Context-aware energy usage profiles  Actionable suggestions to save energy  Automatic actions? Analysis for neighborhood, municipal, etc.  Community-oriented energy savings  Planning purpose  Prediction, optimization 3

4 Data Collection (Observe) Fine-grain instrumentation:  Power usage + environment & human activities  Multiple types of devices:  Wireless plug meters  Smartphones w/ bluetooth  Multiple targets:  Light, heating, air conditioning, plug load Communication of data  Privacy-preserving  Energy efficiency  Filtering, aggregation, compression 4

5 Data Analysis What data?  Real-time streams vs. archives  Privacy vs. details Where to run the analysis?  Cloud vs. locally in the building (smart phones? Game console? sensors?) What kind of analysis?  Event detection  Signal processing  Per-house analysis  Aggregation  Abnormal behavior detection How to model? Per-user per-context profiles? How to efficiently support the processing?  Scalability, energy efficiency How to support a large number of users? A common platform?  Many end (home) users, planners 5

6 Actions Schedule energy usage  E.g. electric vehicle, washing machine Capacity planning Peer-to-peer energy sharing  Solar panel, renewable energy Partnering and understanding the possible actions  Flexible data management for supporting the actions 6

7 Test Bed DOE:  Energy audit for typical building types  Data are available  But mainly coarse-grain Energy lab / green lab  Modeling after PlanetLab 7

8 Challenges Data query frameworks Enable variety of action processing Scale-up + out Energy efficiency Privacy 8


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