OBSERVE: Occupancy-based system for efficient reduction of HVAC energy

Heating, cooling and ventilation accounts for 35% energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning,...

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Veröffentlicht in:2011 10th International Conference on Information Processing in Sensor Networks S. 258 - 269
Hauptverfasser: Erickson, V L, Carreira-Perpinan, M A, Cerpa, A E
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2011
Schlagworte:
Buildings
Data models
Energy savings
Hidden Markov models
HVAC
Load modeling
Markov processes
Occupancy
Predictive models
Ventilation
ISBN:9781612848549, 1612848540
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Zusammenfassung:Heating, cooling and ventilation accounts for 35% energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning, we require knowledge of occupancy. This paper shows how real time occupancy data from a wireless sensor network can be used to create occupancy models which in turn can be integrated into building conditioning system for usage based demand control conditioning strategies. Using strategies based on sensor network occupancy model predictions, we show that it is possible to achieve 42% annual energy savings while still maintaining American Society of Heating, Refrigerating and Air-Conditioning (ASHRAE) comfort standards.
ISBN:9781612848549
1612848540