Occupant behavior is one of the major factors influencing building energy consumption and contributing to uncertainty in building energy use prediction and simulation. Currently the understanding of ...occupant behavior is insufficient both in building design, operation and retrofit, leading to incorrect simplifications in modeling and analysis. This paper introduced the most recent advances and current obstacles in modeling occupant behavior and quantifying its impact on building energy use. The major themes include advancements in data collection techniques, analytical and modeling methods, and simulation applications which provide insights into behavior energy savings potential and impact. There has been growing research and applications in this field, but significant challenges and opportunities still lie ahead.
A method of defining occupants' window opening behaviour patterns in simulation programs, based on measurements is proposed.
Occupants' window opening behaviour has a strong effect on indoor ...environment and the energy consumed to sustain it. Only few models of window opening behaviour exist and these are solely based on the thermal indoor/outdoor environment. Consequently, users of simulation software are often left with little or no guidance for the modelling of occupants' window opening behaviour, resulting in potentially large discrepancies between real and simulated energy consumption and indoor environment.
Measurements of occupant's window opening behaviour were conducted in 15 dwellings in Denmark during eight months. Indoor and outdoor environmental conditions were monitored in an effort to relate the behaviour of the occupants to the environmental conditions. The dwellings were categorized in four groups according to ventilation type (natural/mechanical) and ownership (owner-occupied/rented) in order to investigate common patterns of behaviour. Logistic regression was used to infer the probability of opening and closing a window.
The occupants' window opening behaviour was governed by different but distinct habits in each dwelling. However, common patterns were also identified in the analysis: Indoor CO2 concentration (used as indicator of indoor air quality) and outdoor temperature were the two single most important variables in determining the window opening and closing probability, respectively.
The models could be implemented into most simulation programs, which would enable a better chance of mimicking the behaviour of the occupants in the building and thus simulating the indoor environment and energy consumption correctly.
•We inferred four models of occupants' window opening behaviour.•The models were inferred based on measurements of window position, indoor environment and weather in residential buildings.•Measurements revealed different but distinct habits between dwellings.•Models can be implemented in building energy performance simulations programmes, to increase validity of simulation results.
Extended literature reviews confirm that the accurate evaluation of energy-related occupant behaviour is a key factor for bridging the gap between predicted and actual energy performance of ...buildings. One of the key energy-related human behaviours is window control behaviour that has been modelled by different probabilistic modelling approaches. In recent years, Bayesian Networks (BNs) have become a popular representation based on graphical models for modelling stochastic processes with consideration of uncertainty in various fields, from computational biology to complex engineering problems. This study investigates the potential applicability of BNs to capture underlying complicated relationships between various influencing factors and energy-related behavioural actions of occupants in buildings: in particular, window opening/closing behaviour of occupants in residential buildings is investigated. This study addresses five key research questions related to modelling window control behaviour: (A) variable selection for identifying key drivers impacting window control behaviour, (B) correlations between key variables for structuring a statistical model, (C) target definition for finding the most suitable target variable, (D) BN model with capabilities to treat mixed data, and (E) validation of a stochastic BN model. A case study on the basis of measured data in one residential apartment located in Copenhagen, Denmark provides key findings associated with the five research questions through the modelling process of developing the BN model.
•The applicability of the Bayesian Network Framework to model window control behaviour is demonstrated.•A procedure for developing a BN model with full exploitation of mixed data is presented.•Key variables that mostly impact window control actions are highlighted.•The window opening action is more suitable as a target variable to model window control behaviour.
The human interaction with the building is a key cause of uncertainty when predicting energy consumption of buildings. Building occupants affect building energy use directly and indirectly by ...interacting with building energy systems, for example, by adjusting thermostats, switching lights on/off, using electrical devices and opening/closing windows. The occupants’ daily activity profiles and occupancy patterns clearly shape the timing and magnitude of energy demand in households. Modelling energy-related human activities throughout the day, therefore, is a crucial task for prediction of energy use and, consequently, to reduce the gap between real and predicted building energy use.
This study modelled data gathered in the diary-based Danish Time Use Survey (TUS) 2008/09 of 9640 individuals from 4679 households. Individuals’ daily activities were logged in 10-min time increments for 24 h, starting and ending at 04:00, during both weekdays and weekends. The aims of this study were to (i) profile energy-related daily activities of occupants during different seasons and weekdays/weekends (ii) investigate time-related characteristics of activities such as starting and ending times and durations, and (iii) profile occupancy patterns for weekdays/weekends for different household types. The outcomes provide valuable input for building energy simulation for bridging the gap between simulated and real energy consumption in the Danish residential sector; typical occupancy profiles for different household types for different days of the week are freely available online 1.
Occupants’ interactions with the building envelope and building systems can have a large impact on the indoor environment and energy consumption in a building. As a consequence, any realistic ...forecast of building performance must include realistic models of the occupants’ interactions with the building controls (windows, thermostats, solar shading etc.).
During the last decade, studies about stochastic models of occupants’ behaviour in relation to control of the indoor environment have been published. Often the overall aim of these models is to enable more reliable predictions of building performance using building energy performance simulations (BEPS). However, the validity of these models has only been sparsely tested.
In this paper, stochastic models of occupants’ behaviour from literature were tested against measurements in five apartments. In a monitoring campaign, measurements of indoor temperature, relative humidity and CO2 concentration was measured in the living room and bedroom at five minute intervals in five apartments with similar layout in a building located in Copenhagen, Denmark. Outdoor temperature, relative humidity, wind speed and solar radiation were obtained from a weather station close by.
The stochastic models of window opening and heating set-point adjustments were implemented in the BEPS tool IDA ICE. Two apartments from the monitoring campaign were simulated using the implemented models and the measured weather data. The results were compared to measurements from the monitoring campaign to get an estimate of the forecast’s realism.
The simulations resulted in realistic predictions in a sense that the measured values were within or close to the range of the simulated values. The variation in the simulated and measured variables between apartments and over time was similar. However, comparisons of the average stochastic predictions with the measured temperatures, relative humidity and CO2 concentrations revealed that the models did not predict the actual indoor environmental conditions well.
The heat transfer coefficients between radiant surfaces and room are influenced by several parameters: surfaces temperature distributions, internal gains, air movements.
The aim of this paper is to ...evaluate the heat transfer coefficients between radiant ceiling and room in typical conditions of occupancy of an office or residential building. Internal gains were therefore simulated using heated cylinders and heat losses using cooled surfaces. Evaluations were developed by means of experimental tests in an environmental chamber.
Heat transfer coefficient may be expressed separately for radiation and convection or as one total parameter, but this choice may lead to different considerations about thermal performance of the system. In order to perform correct evaluations, it is therefore extremely important to use the proper reference temperature.
The obtained values confirm tendencies found in the literature, indicating limitations and possibilities of radiant ceiling systems improvement.
Energy-related occupant behavior in buildings is difficult to define and quantify, yet critical to our understanding of total building energy consumption. Part I of this two-part paper introduced the ...DNAS (Drivers, Needs, Actions and Systems) framework, to standardize the description of energy-related occupant behavior in buildings. Part II of this paper implements the DNAS framework into an XML (eXtensible Markup Language) schema, titled ‘occupant behavior XML’ (obXML). The obXML schema is used for the practical implementation of the DNAS framework into building simulation tools. The topology of the DNAS framework implemented in the obXML schema has a main root element OccupantBehavior, linking three main elements representing Buildings, Occupants and Behaviors. Using the schema structure, the actions of turning on an air conditioner and closing blinds provide two examples of how the schema standardizes these actions using XML. The obXML schema has inherent flexibility to represent numerous, diverse and complex types of occupant behaviors in buildings, and it can also be expanded to encompass new types of behaviors. The implementation of the DNAS framework into the obXML schema will facilitate the development of occupant information modeling (OIM) by providing interoperability between occupant behavior models and building energy modeling programs.
•An occupant behavior XML schema obXML is developed based on the DNAs framework.•The obXML provides a standardize representation of occupant behavior in buildings.•The obXML is used to describe windows opening and blinds closing behavior as examples.•The obXML is generic and flexible for expansion to describe new occupant behavior.•The obXML schema is planned to integrate with building information modeling.
This study presents results from an interdisciplinary survey assessing contextual and behavioral factors driving occupants' interaction with building and systems in offices located across three ...different Mediterranean climates in Turin (Northern), Perugia (Central), and Rende (Southern) Italy. The survey instrument is grounded in an interdisciplinary framework that bridges the gap between building physics and social science environments on the energy- and comfort-related human-building interaction in the workspace. Outcomes of the survey questionnaire provide insights into four key learning objectives: (1) individual occupant's motivational drivers regarding interaction with shared building environmental controls (such as adjustable thermostats, operable windows, blinds and shades, and artificial lighting), (2) group dynamics such as perceived social norms, attitudes, and intention to share controls, (3) occupant perception of the ease of use and knowledge of how to operate control systems, and (4) occupant-perceived comfort, satisfaction, and productivity. This study attempts to identify climatic, cultural, and socio-demographic influencing factors, as well as to establish the validity of the survey instrument and robustness of outcomes for future studies. Also, the paper aims at illustrating why and how social science insights can bring innovative knowledge into the adoption of building technologies in shared contexts, thus enhancing perceived environmental satisfaction and effectiveness of personal indoor climate control in office settings and impacting office workers' productivity and reduced operational energy costs.
•A questionnaire survey assesses contextual and behavioral factors driving human-building interaction in office spaces.•The survey uses an interdisciplinary framework integrating building physics and social psychology.•The survey has 1160 valid responds from office occupants in three climates of Italy.•A general dissatisfaction emerged over the shared control of thermostat settings in office spaces.•The survey provides insights into motivational drivers, group dynamics, ease of use and knowledge of control systems.
Design guidelines envisage that floor heating can be used together with displacement ventilation (DV), provided that the supply air is not overly heated before it can reach heat and contaminant ...sources. If this is not controlled a mixing flow pattern could occur in the room. The use of floor cooling with DV is also considered possible, although draught risk at ankle level and vertical air temperature differences must be controlled carefully, because they could increase.
Few studies on these topics were found in the literature.
An indoor environmental chamber was set up to obtain measurements aimed at analysing the possibilities and limitations of combining floor heating/cooling with DV. Air temperature profiles, air velocity profiles, surface temperatures and ventilation effectiveness were measured under different environmental conditions that may occur in practice. These values were compared to equivalent temperature measurements obtained using a thermal manikin.
The measurements show that floor heating can be used with DV, obtaining high ventilation effectiveness values. A correlation between the floor heating capacity and the air temperature profile in the room was found. Measurements showed that floor cooling does not increase draught risk at ankle level, although it does increase vertical air temperature differences.
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