Wireless, collaborative virtual sensors for thermal comfort

Ploennigs, Joern; Hensel, Burkhard; Kabitzsch, Klaus

doi:10.1145/1878431.1878449

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…Temperature. Thermal comfort is a complex measurement that depends on many aspects such as temperature, humidity, air velocity, occupants' clothing and activity [Ploennigs et al 2010;Pfafferott et al 2007]. The most common comfort measurement is Fanger's Predicted Mean Vote PMV as standardized in ISO 7730 [Olesen and Parsons 2002].…”

Section: Table I Pmv Comfort Classesmentioning

confidence: 99%

Occupancy Modeling and Prediction for Building Energy Management

Erickson

Carreira-Perpiñán

Cerpa

2014

ACM Trans. Sen. Netw.

127

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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 article 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 Engineers (ASHRAE) comfort standards.

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Section: Table I Pmv Comfort Classesmentioning

confidence: 99%

Occupancy Modeling and Prediction for Building Energy Management

Erickson

Carreira-Perpiñán

Cerpa

2014

ACM Trans. Sen. Netw.

127

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“…BAS [5] is an application programming interface and runtime that enables writing portable code by providing methods to explicitly and implicitly handle differences in building designs. A key insight of BAS is the use of fuzzy, relativistic queries to allow authors to express their high-level intent in a way that is inherently portable, e.g., "turn off the lights for top floor cubicles near windows," as well as supporting programmatic exploration [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26] of a building's specific components, allowing applications to explicitly handle building differences. Thus programmers can alternate between macro-and micro-level views of the building (e.g., "lights on the top floor" vs. "Light Relay 1023") to express both general intentions and specific actions.…”

Section: Building Applicationsmentioning

confidence: 99%

Enabling advanced environmental conditioning with a building application stack

Taneja

Krioukov

Dawson-Haggerty

et al. 2013

2013 International Green Computing Conference Proceedings

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Abstract-Buildings are an important venue in which to apply information technology to increase sustainability. There is enormous potential for building-focused applications, both for classical uses like modeling or fault detection as well as innovative ones like occupant-driven control or grid-aware energy management. However, existing building control systems suffer from antiquated, architectures that hinder application development by siloing valuable sensing data, limiting extensibility via custom designs, and perpetuating arcane and inconsistent naming schemes. To address these deficiencies, a new architecture is emerging to enable application development for buildings by democratizing sensor data, constructing a framework for reliable, fault-tolerant operation of applications, and establishing an application programming interface for encouraging portability throughout the building stock. In this paper, we show that this building application stack enables advanced environmental conditioning applications. We observe the growing importance of applications that integrate sensors and actuators from the building infrastructure with those from "add-on" networks, and show how this design pattern is further empowered by the architecture. To prove the efficacy of the approach, we implement two advanced environmental conditioning applications on a large, commercial building that was not designed for either of them: a demandcontrolled ventilation (DCV) system for balancing air quality considerations and energy use in conference and class room settings and a demand-controlled filtration (DCF) system for conserving recirculating fan energy in an intermittently occupied cleanroom setting. The DCV application is able to reduce air quality threshold violations by over 95% and concurrently reduce energy consumption by over 80%, while the DCF application can reduce recirculating fan power consumption by half with no repercussions on air quality when the room is occupied. Further, the portability of these applications highlights the potential of the architecture to enable widespread and rapid application development throughout the building stock.

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“…Let us consider a thermal comfort control that bases on a virtual sensor using only temperature [2]. A controller working on the virtual measurement 'thermal comfort' is then usually also providing a 'virtual' actuation cOlmnand that needs to be translated back to the HVAC system.…”

Section: Virtual and Defusion Actuatorsmentioning

confidence: 99%

“…But, even if a body sensor may provide significant and accurate data about thermal comfort of a person, it requires no specific controller, if the oc cupied room has only one large floor heating system. However, if the room provides a personalized HVAC system and a good localization, but only one unsignificant temperature sensor, a virtual sensor could be used to improve the information [2].…”

Section: Controller Classificationmentioning

confidence: 99%

“…This flexibility bears much more opportunities for the sys tems such as improving the robustness by added redundancy. Furthermore it allows data-based services such as virtual sensors [2], sensor fusion [3] and model-predictive control [4]. However, it also increases complexity from systems with a static structure to systems that have to dynamically adjust to the available sensor information.…”

Section: Introductionmentioning

confidence: 99%

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