Occupant Comfort, Productivity, and Personal Control in Twenty Air

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Occupant Comfort, Productivity, and Personal Control in Twenty Air. Conditioned Office Buildings. Jared Langevin1,*, Jin Wen1, Sean Hsieh2 , Davor Novosel3, ...
Occupant Comfort, Productivity, and Personal Control in Twenty Air Conditioned Office Buildings Jared Langevin1,*, Jin Wen1, Sean Hsieh2 , Davor Novosel3, and Michael S. Waring1 1

Department of Civil, Architectural and Environmental Engineering, Drexel University Mechanical Engineering Department, University of Nevada Las Vegas 3 National Energy Management Institute 2

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Corresponding email: [email protected]

Keywords: Occupant Satisfaction, Comfort Adaptation, High Performance Buildings

1 Introduction Recent research has suggested that inadequate consideration of occupant variables during the building design process can lead to problems with overall occupant well being in the field and correspondingly high energy use (Steemers, 2010; Bordass et al. 2001). This study analyzes a field dataset of occupant responses with the objective of identifying those physical and psychological aspects of the working environment that are most critical to achieving good occupant satisfaction and productivity. Specifically, the relative impact of various individual environmental variables (temperature, humidity, lighting, etc.) on occupant assessments of overall acceptability and productivity is examined, as well as the degree to which an occupant’s ability to control his or her environment associates with variablespecific and overall environmental acceptability.

2 Materials/Methods The analysis concerns a series of occupant surveys that were collected from twenty airconditioned office buildings by the National Center for Energy Management and Building Technologies (http://www.ncembt.org). Seven of these buildings were considered “normative” and the rest were “high performance”, where the threshold between the two classifications was defined as a LEED Silver rating. The buildings are located across the United States and were all either constructed or renovated after 1987. In total, the field dataset includes 662 occupant responses from the twenty buildings surveyed. The following response information was given a primary focus:

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Five-interval acceptability ratings for seven individual environmental variables (temperature, humidity, lighting, air freshness, draft, sound, and smell) and overall (1- “strongly disagree” condition is acceptable to 5-“strongly agree”). Five-interval ratings of the degree to which unfavorable conditions of each of the seven environmental variables negatively affect occupant self-assessed productivity (1“never affects” to 5 – “always affects”). Assessments of occupants’ ability to control temperature, artificial and natural lighting, and privacy in personal work areas, coded into “direct control” or “indirect/no control”.

Statistical analysis was performed on the data in PASW Statistics Version 18.0. The procedure used Q-Q Plots to check for normally distributed data; bivariate Pearson correlations to identify associations between occupant assessments of satisfaction, productivity, and control; T-Tests of means to check for significant differences between acceptability and productivity assessments of certain occupant groupings; and multiple regressions to assess the relative influence of each individual environmental variable on overall satisfaction rating. As a part of this process, occupants were sorted into different groups according to reported level of personal control, gender, and building type.

3 Results The Q-Q Plots revealed that all of the occupant response variables under study were either normally or log-normally distributed. In particular, each of the acceptability responses was well approximated by a normal distribution and thus could be included in multiple linear

regressions. Regression of all seven environmental acceptability variables against overall acceptability showed that temperature mattered significantly more to overall acceptability than did any other single variable, with a t-statistic of 18.2 that was much higher than that of the next most important variable, sound acceptability, which yielded a t-statistic of 4.59. Overall, the four individual acceptability variables of temperature, sound, lighting, and draft achieved high statistical significance in the regression (p