Inferring personalized visual satisfaction profiles in daylit offices from comparative preferences using a Bayesian approach

Xiong, Jie; Tzempelikos, Athanasios; Bilionis, Ilias; Awalgaonkar, Nimish M.; Lee, Seungjae; Konstantzos, Iason; Sadeghi, Seyed Amir; Karava, Panagiota

doi:10.1016/j.buildenv.2018.04.022

Cited by 28 publications

(8 citation statements)

References 63 publications

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“…This paper first ranked the parameters that may influence occupant vision, and eventually, the four most influential parameters were UVI, DGI, LR, and SP. This finding agrees with prior studies, explaining why many scholars focus on these indicators [5,69]. Likewise, these factors can be changed easily by specific light and unified light in the interior environment, which benefits our adjustment in the operation and maintenance period.…”

Section: Discussionsupporting

confidence: 91%

“…Nonetheless, because the light environment comfort cannot be accurately quantified, the accuracy of the model is barely satisfactory. Thus, Jie Xiong et al deduced a personalized visual satisfaction model in a private office through Bayesian analysis to coordinate personal satisfaction and energy savings [5]. However, the data needed for the training model are not well explained in this paper, such as how many data points are needed to complete the training of the personalized visual satisfaction model or what types of data and indicators are needed for training.…”

Section: Visual Comfort Modelmentioning

confidence: 96%

“…The participants scored the overall visual comfort, and the score was divided into 3 degrees (+1, 0, −1), representing from dim to bright for quantitative processing. There is a substantial amount of literature on visual environment evaluation [5], and the three-point scale applied in this study serves to solve the evaluation standard. The mid-point of the scale is "Satisfactory", which is a diverse type of assessment from judging darkness or brightness.…”

Section: Procedures and Data Collectionmentioning

confidence: 99%

“…As a part of the indoor environment, visual comfort is an integral part of building energy consumption, personal health, work efficiency, and satisfaction, which has also attracted many scholars [4]. In the interior environment of buildings, individuals have preferences for visual environment [5][6][7]. Research has confirmed that when occupants can adjust the illuminance of task area, it positively affects satisfaction with environmental conditions [8], the quantity and quality of lighting [9], mood [10], and productivity [11].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, some research concluded that vertical illuminance is better than common indicators such as horizontal illuminance, luminance ratio, and DGI [14]. Although preventing glare is essential, achieving general visual comfort will not be satisfied with the optimal visual environment [5]. In contrast, without only applying glare, considering individual preference may lead to optimizing the visual environment.…”

Section: Introductionmentioning

confidence: 99%

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Research on a Visual Comfort Model Based on Individual Preference in China through Machine Learning Algorithm

Pan

2021

Sustainability

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Recently, decreasing energy consumption under the premise of building comfort has become a popular topic, especially visual comfort. Existing research on visual comfort lacks a standard of how to select indicators. Moreover, studies on individual visual preference considering the interaction between internal and external environment are few. In this paper, we ranked common visual indicators by the cloud model combined with the failure mode and effect analysis (FMEA) and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS). Unsatisfied vertical illuminance, daylight glare index, luminance ratio, and shadow position are the top four indicators. Based on these indicators, we also built the individual visual comfort model through five categories of personalized data obtained from the experiment, which was trained by four machine learning algorithms. The results show that random forest has the best prediction performance and support vector machine is second. Gaussian mixed model and classification tree have the worst performance of stability and accuracy. In addition, this study also programmed a BIM plug-in integrating environmental data and personal preference data to predict appropriate vertical illuminance for a specific occupant. Thus, managers can adjust the intensity of artificial light in the office by increasing or decreasing the height of table lamps, saving energy and improving occupant comfort. This novel model will serve as a paradigm for selecting visual indicators and make indoor space be tailored to meet individual visual preferences.

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Section: Discussionsupporting

confidence: 91%

Section: Visual Comfort Modelmentioning

confidence: 96%

Section: Procedures and Data Collectionmentioning

confidence: 99%