Comparison of low-energy office buildings in summer using different thermal comfort criteria

Pfafferott, Jens; Herkel, Sebastian; Kalz, Doreen; Zeuschner, Andreas

doi:10.1016/j.enbuild.2007.02.005

Cited by 81 publications

(40 citation statements)

References 2 publications

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“…764). Results show how user satisfaction corresponds to control abilities for users which are supported by results in Pfafferott et al (2007). Actual temperature and temperature sensations had less effect on user satisfaction in this study.…”

Section: Concluding Discussionsupporting

confidence: 68%

“…However, research results from low-energy buildings in Web of Science using this questionnaire are lacking. Also in Germany, questionnaires have been used in research about user satisfaction, but in office buildings (Pfafferott et al, 2007;Wagner et al, 2007). The method has been developed by University of California's Center for Environmental Design Research, Berkeley and according to the authors it addresses "all relevant aspects of occupant satisfaction with indoor environments" (Wagner et al,p.…”

Section: Concluding Discussionmentioning

confidence: 99%

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The energy efficiency of onboard hydrogen storage

Jensen

Vestbø

et al. 2007

Journal of Alloys and Compounds

134

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Section: Concluding Discussionsupporting

confidence: 68%

Section: Concluding Discussionmentioning

confidence: 99%

The energy efficiency of onboard hydrogen storage

Jensen

Vestbø

et al. 2007

Journal of Alloys and Compounds

134

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“…Even though thermal comfort is a complex measurement that depends on many aspects such as temperature, humidity, air velocity, occupants clothing and activity [67]. Each of the studies in this survey simulates its own control strategy with different parameters, such as the physical description of a building (including walls, floors, roofs, windows and doors, each with associated construction properties such as R-Value of materials used, size of walls, location and type of windows), the descriptions of mechanical equipment (heating and cooling), the mechanical ventilation schedules, the occupancy schedule, the other household equipment, air temperature, radiant temperature, humidity, air velocity, occupants clothing and activity, and so on.…”

Section: Energy Saving Potentialmentioning

confidence: 99%

Energy intelligent buildings based on user activity: A survey

Nguyen

Aiello

2013

Energy and Buildings

605

297

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a b s t r a c tOccupant presence and behaviour in buildings has been shown to have large impact on heating, cooling and ventilation demand, energy consumption of lighting and appliances, and building controls. Energyunaware behaviour can add one-third to a building's designed energy performance. Consequently, user activity and behaviour is considered as a key element and has long been used for control of various devices such as artificial light, heating, ventilation, and air conditioning. However, how are user activity and behaviour taken into account? What are the most valuable activities or behaviours and what is their impact on energy saving potential? In order to answer these questions, we provide a novel survey of prominent international intelligent buildings research efforts with the theme of energy saving and user activity recognition. We devise new metrics to compare the existing studies. Through the survey, we determine the most valuable activities and behaviours and their impact on energy saving potential for each of the three main subsystems, i.e., HVAC, light, and plug loads. The most promising and appropriate activity recognition technologies and approaches are discussed thus allowing us to conclude with principles and perspectives for energy intelligent buildings based on user activity.

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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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Comparison of low-energy office buildings in summer using different thermal comfort criteria

Cited by 81 publications

References 2 publications

The energy efficiency of onboard hydrogen storage

The energy efficiency of onboard hydrogen storage

Energy intelligent buildings based on user activity: A survey

Occupancy Modeling and Prediction for Building Energy Management

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