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.
Highlights
IntroductionPeople usually spend approximately 90% of their time living in buildings [1]
Buildings, where people live, are closely related to the lives of people
We used confusion matrix and area under the curve (AUC) with its standard deviation to assess prediction performance, and the results indicate that random forest (RF) had the best prediction performance
Summary
People usually spend approximately 90% of their time living in buildings [1]. Energy consumed by buildings accounts for about 40% of global energy consumption. According to a report of the Joint Research Center of the European Union, the energy consumption for artificial lighting accounts for 14% in the EU and 19% in the world [2]. In 2018, the energy consumption of China’s construction industry was 2.147 billion tce, accounting for 46.5%. During the operation and maintenance, energy consumption was 1 billion tce, which accounts for 46.6% of the energy consumption of the building industry [3]. As a prominent building system, the lighting system consumes approximately 14% of energy in the operation phase. Reducing the energy consumption of the lighting system signifies reducing the overall energy consumption of the building. Energy consumption in the building sector must be reduced
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