Bayesian estimation of visual discomfort

Lindelöf, D.; Morel, Nicolas

doi:10.1080/09613210701544061

Cited by 35 publications

(8 citation statements)

References 12 publications

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“…The PMP does not include a maximum recommended lighting level for illuminance, but we feel that overlighting is an issue that needs to be addressed. Lindelöf and Morel have shown through Bayesian estimation based on lab studies that 2500 lx is the upper illuminance level at which the probability of occupant discomfort jumps up [66]. While an upper illuminance level is important to consider for occupant visual comfort, ideally the electric light contribution toward illuminance (including both task and general lighting) in an office environment should be zero when sufficient daylight exists or kept at or slightly above the recommended minimums outlined in the PMP when sufficient daylight is not available.…”

mentioning

confidence: 99%

Indoor environmental quality assessment models: A literature review and a proposed weighting and classification scheme

Heinzerling

Schiavon

Webster

et al. 2013

Building and Environment

237

160

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This paper explores the existing literature on indoor environmental quality (IEQ) evaluation models and proposes a new weighting and classification scheme. Studies that attempt to provide IEQ assessment of commercial buildings through a scoring system are reviewed and critiqued. Objective and subjective evaluation methods and correlations are discussed. The use of assessment categories (classes) in IEQ models is critiqued and an argument is proposed against their adoption. IEQ weighting schemes are summarized and compared against a newly developed scheme based on 52,980 occupant responses in office buildings. A binary assessment classification scheme is proposed in alignment with the ASHRAE/CIBSE/USGBC Performance Measurement Protocols for Commercial Buildings.

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mentioning

confidence: 99%

Indoor environmental quality assessment models: A literature review and a proposed weighting and classification scheme

Heinzerling

Schiavon

Webster

et al. 2013

Building and Environment

237

160

View full text Add to dashboard Cite

show abstract

“…As mentioned earlier, Newsham (1994) used the solar intensity of 233 W/m² as the predictor variable whereas Reinhart (2004) found 50 W/m². Conversely, Lindelöf and Morel (2008) adopted 800 to 1200 lux on the horizontal work-plane to estimate visual discomfort in small offices for both blinds and lighting controls in their probability model, which was consistent with a probability model developed by Haldi and Robinson (2010). These findings were not applied in this study for the illuminance thresholds; since this study assumed that the vertical illuminance would also be a significant predictor variable.…”

Section: Stage 3: Thresholds and Blind Schedulingsupporting

confidence: 58%

A Methodology To Simulate Annual Blind Use In Large Open Plan Offices

Pham¹,

Wagdy²,

Isoardi³

et al.

Building Simulation Conference Proceedings

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Occupant behaviour (OB) models relating to daylighting and blind use have been developed for cellular office configurations. However, this is not the case for large open plan offices that represent different spatial, daylighting and occupancy characteristics. This paper presents a methodology to simulate annual blind use based on the daylight availability for large open plan offices. A novel multi-directional illuminance grid was used to simulate annual blind use; to account for multiple viewpoints and occupants. The results found that this method can be used to predict annual blind use in large open plan offices to improve building performance simulations and occupancy models.

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“…Aoki et al applied Bayesian Network to analyze how local thermal comfort would influence whole body thermal comfort level [40]. In the visual environment area, Lindelöf used the Bayesian approach to estimate the visual discomfort probability as a function of the illuminance under the office settings [41]. Sadeghi et al applied Bayesian classification and inference models to develop probability distributions of occupants' preference about the visual environment (prefer darker, brighter, or no change) [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

Wang

Hong

2020

Renewable and Sustainable Energy Reviews

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A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants' comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4°C for the cooling mode with a median of 23.7°C, and between 20.5 and 24.9°C for the heating mode with a median of 22.7°C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2-3°C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking.

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Bayesian estimation of visual discomfort

Cited by 35 publications

References 12 publications

Indoor environmental quality assessment models: A literature review and a proposed weighting and classification scheme

Indoor environmental quality assessment models: A literature review and a proposed weighting and classification scheme

A Methodology To Simulate Annual Blind Use In Large Open Plan Offices

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

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