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

Abstract

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.

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

“…where x(k) is the output vector of the hidden layer, w 1 is the connection weight matrix from the input layer to the hidden layer, w 2 is the connection weight matrix from the hidden Land 2024, 13, 367 31 of 34 layer to the output layer, and b 1 and b 2 are the number of deviations in the hidden and output layers [77]. The transfer algorithm used between the hidden and output layers is as follows in Equations (A13) and (A14):…”

Visual Perception Optimization of Residential Leisure Space in Cold Regions Using Virtual Reality and Machine Learning

“…where 𝑥(𝑘) is the output vector of the hidden layer, 𝑤 is the connection weight matrix from the input layer to the hidden layer, 𝑤 is the connection weight matrix from the hidden layer to the output layer, 𝑏 and 𝑏 are the number of deviations in the hidden and output layers [57]. The transfer algorithm used between the hidden and output layers is as follows:…”

Quantifying of Vision through Polymer Dispersed Liquid Crystal Double-Glazed Window

“…The corresponding applied voltage at the preferred contrast v PVR can be defined by substituting Equation (12) into Equation (11), as shown below:…”

“…Ma et al [11] surveyed the preferences of a set of students regarding the vision comfort and discomfort glare of a study area for six months. The authors used the output data to create a building information model (BIM), which was translated into an algorithm for a machine learning system, and the resultant algorithm was used to control a proposed window shading system.…”