Investigation on window opening and closing behavior in schools through measurements and surveys: A case study in Budapest

In recent years, research has emerged to quantitatively and qualitatively understand occupants' interactions with buildings. However, there has been surprisingly little research on building interfaces and how their design, context (e.g., location), and underlying logic impact their usability and occupants' perceived control, as well as the resulting comfort and energy performance. Research is needed to better understand how occupants interact with building interfaces in both commercial and residential applications; both applications are important to address as there are many differences in interface types, level of control and understanding, and even expectations of engagement. This paper provides a cursory review and discussion of select common building interfaces: windows, window shades/blinds, thermostats, and lighting controls. The goal of this paper is to review literature related to these human-building interfaces to explore interface characteristics, current design and use challenges, and relationships between building interfaces and occupants. Human-building interface interactions are complex, more research is needed to understand design, use, and characteristics. Common themes emerged throughout the literature review to explain occupant interactions (or lack of interactions) with building interfaces, which included thermal and visual comfort, ease and access of control, interface/control placement, poor interface/control design, lack of understanding, and social-behavioral dynamics.

Section: Rq1: What Influences Behavior?mentioning

confidence: 99%

Section: Rq1: What Influences Behavior?mentioning

confidence: 99%

A review of select human-building interfaces and their relationship to human behavior, energy use and occupant comfort

Day

McIlvennie

Brackley

et al. 2020

“…Following the Köppen climate classification scheme, the majority of the analyzed window opening models were developed in temperate climate zones Cfb (43%), Cfa (23%), Csa (2%), while the remaining in continental climate Dwa (16%) and Dfb (16%). Furthermore, most published studies referring to occupant window behavior have been carried out in European countries [37,94,113,117,[119][120][121][122][123][124][125][126]102,[127][128][129][130][131][132][133][105][106][107][109][110][111][112]. Since window operation enables physical connection with the outdoor environment, it can be directly influenced by different conditions such as the atmospheric environment but also contextual factors such as routine/habits [134] and individual preferences [135].…”

Section: Window Operationmentioning

confidence: 99%

Modeling occupant behavior in buildings

Carlucci

Simone

Firth

et al. 2020

150

In the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants' actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the Revised Manuscript with No Changes MarkedClick here to view linked References data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.

“…In the past decade, significant progresses and advanced technologies have been achieved in terms of above aspects, expect occupant behavior [1][2]. Some methods, such as the survey-based approach [3][4][5][6][7][8][9], Data-driven approaches [10][11], and building performance simulation (BPS) [12] have been used to evaluate the impact of occupant behavior on buildings performance.…”

Section: State Of the Artmentioning

confidence: 99%

A model based on Gauss Distribution for predicting window behavior in building

Pan

Han

Wei

et al. 2019

Modeling of window behavior is a key component for building performance simulation, due to the significant impact of opening/closing windows on indoor environment and energy consumption. The predictions of existing models cannot well reflect actual window behavior, the prediction accuracy still needs to be improved. The Gauss distribution model is a new machine-learning technique which has achieved successful applications in many fields because of its special advantages (i.e. simple structure, strong operability and flexible nonparametric inference ability) compared to existing models. This paper presents results from a study using the Gauss distribution model to predict window behavior in office building. The data used in this study were from a real building located in Beijing, China, and covered two transitional seasons (from October 1 to November 15, 2014 and from March 15 to May 16, 2015), when natural ventilation was fully applied. When modeling, three types of input variables, i.e., indoor temperature, outdoor temperature and their combination were used. This work validates the importance of selecting suitable input variables when developing Gauss distribution model. This study also compared the prediction performance between the Gauss distribution modeling approach and the Logistic regression modeling approach, which is the most popular method used to model occupant window behavior in buildings. The results showed that Gauss distribution models could provide higher prediction accuracy, with 9.5% higher than Logistic regression model when using suitable inputs. This paper provided a novel modeling method that can be used to predict window states more accurately in office buildings.