Frontiers in Civil Engineering

The literature emphasises the important role that occupants play regarding the energy performance of buildings. Scholars have applied several methods to assess occupants’ preferences and practices in their field studies. Technological innovations such as Internet-of-Things (IoT) may capture valuable objective information that can be translated into mathematical models. Such models are vital in Building Performance Simulation (BPS) practices as they are expected to reduce performance gaps between expected and real energy use in buildings during operational phase. However, datadriven models strictly related to physical parameters exclude essential subjective information like occupant preferences and needs. There is enough evidence showing that individual differences impact on thermal preferences and levels of comfort indoors, which must also be considered in occupant behaviour studies. Aside from individual preferences, there is also social influence when occupants share spaces and the control of building systems. Several methods commonly used in social science studies are expected to incorporate the needed subjective information in this field if properly used. Therefore, this chapter explores the potentials of combining objective information gathered from technological innovations with subjective inputs obtained through qualitative methods.

People’s behaviour can significantly impact both the energy consumption and the indoor thermal environment of the buildings, and of particular interest is their window opening behaviour. A better understanding of why, when and how occupants open windows is, therefore, essential in the quest to achieve low-carbon buildings. Many studies have sought to answer these questions based on behavioural data measured in actual buildings. This paper introduces existing methods that have been used to monitor occupant window opening behaviour in buildings based on a comprehensive review of literature, as well as for relevant influential factors, and critically discusses the advantages and disadvantages of each method. The review has identified five methods monitoring window usage (i.e. self-recording, electronic recording, observing by surveyors and self-estimating), and each method has its advantages and disadvantages in terms of feasible sample size, monitoring interval and duration, recognition of window states/opening angle, and the relative dynamic nature of behaviour. The aim has been to provide researchers with systematic criteria for selecting a suitable monitoring method for their specific research objectives. Additionally, the paper demonstrates the need for a standard method for monitoring relevant influential factors, as these varied considerably between existing studies with respect to the accuracy, interval and location. Such variation clearly has the potential to influence the ability to perform cross-study comparisons.

Space heating is responsible for a significant share of the energy consumed in European households, and the replacement of appliances with more efficient alternatives can be decisive for meeting the targets set by the European Union for 2030 and 2050. Although an estimated 60% of the heating stock consists of inefficient equipment, users are often not aware of this inefficiency and associated costs. Also, changing and improving the heating systems have been systematically associated with a wide range of effects, such as thermal comfort and improved air quality, which are often termed as co-benefits or ancillary benefits. Previous research has shown that cobenefits can be decisive when users choose a heating solution. This chapter reports on the results obtained in a study conducted in the scope of the EU H2020 HARP research project, in which an international qualitative survey was used to identify, quantify and evaluate the co-benefits associated with heating solutions, to clarify the relevance of the co-benefits in the decision-making process of building users. The results suggest that both the degree of relevance and the willingness to pay for co-benefits vary significantly amongst different national contexts.

The chapter reviews the impact of occupancy in buildings, in particular in thermal comfort and energy efficiency. Concerning the first issue, this chapter will first propose a means to estimate occupancy, the impact of occupation in thermal comfort measured by the Predicted Mean Vote index, and its use for real-time control of HVAC equipment. All data used are measured data of a real university building under normal occupation. The effect of occupancy in energy efficiency will focus on the residential segment, using data of a recent installation of a data acquisition and control system in a household located in the south of Portugal. This work shows that the impact of occupancy in electricity consumption becomes more evident as the electric energy is being desegregated and that the availability of this information by the occupants can be used to improve energy efficiency. Moreover, the use of occupation in the design of electric consumption forecasting methods will also be discussed.

The new directive 2018/844/EU on Energy Performance of Buildings and Energy Efficiency, supports the change of building into smarter, more energy-efficient and include the perspective of the occupants' needs. The knowledge of occupant behaviour is the centre of the balance between the buildings energy efficiency and its indoor environmental quality. This chapter presents a state-of-the-art of the buildings occupant behaviour, presenting the new developments and future trends. It summarises research in which a series of methodologies were developed to supply relevant data to the building management systems (BMS). These methodologies use a monitoring system based on environmental sensors, namely relative humidity, temperature, and carbon dioxide. New methods to detect the occupant actions in the operation of building systems were summarised and compared. The methodologies were based on statistical tools and machine learning techniques. They can be applied to different case studies since they can adapt to the local environment under a self-learning strategy. The drivers of behaviour for the operation of those building systems were also analysed. Two methodologies that allowed to predict the occupant actions taking into account the parameters that influenced the occupant behaviour were described. It was also possible establishing the seasonality of drivers of behaviour. The overall results highlight that the actions, motivations, and impacts of a specific set of occupants performed in building systems can significantly vary depending on the room and on environmental parameters.

Occupancy is one of the main factors to understand the operation and energy consumption of a building due to the variability of human behaviour. However, the variability of human behaviour is not taken into account in many thermal and energy performance studies, causing inconsistencies between simulation results and reality. One of the reasons for these inconsistencies also relies on adopting an opening availability schedule which is strictly limited to the occupancy schedule of a room, especially in residential buildings, which may not represent the reality. Thus, the aim of this study is to investigate the dependency relationship between the room’s occupancy schedule and the operation of openings in residential buildings. Data on occupancy of rooms and openings operation were obtained through a database obtained by means of application of questionnaires in low-income houses in Florianópolis, southern Brazil. Descriptive analysis by means of association measures was performed in order to evaluate the level of relationship between occupancy and openings operation. In addition, cluster analysis was performed to identify different patterns of occupant behaviour in the sample. The main result has shown that the opening operation schedule often does not depend on whether the room is occupied or not and seems to rely more accordingly to a daily routine, such as the time one wakes up or goes to sleep, or leaving and coming back home.

The literature states that the occupancy and related operational characteristics in buildings are key variables that cause the gap between the estimate and actual thermal and energy performance. To address such issue, the objective of this study is to investigate the uncertainties of occupant behaviour in building performance simulation through a probabilistic approach. This case study considers a model of a low-income dwelling in southern Brazil using five different construction for the envelope and with natural or hybrid ventilation. Field survey provided a dataset of uncertainties of the occupant behaviour, which was related to the occupancy of the rooms, operation of openings and use of electric appliances. The EnergyPlus programme was used to conduct the simulations and the R Studio was used for data processing, analysis, and treatment. A global sensitivity analysis was performed, along with an uncertainty analysis. The results showed that the number of occupants, the schedules of occupancy of the bedrooms, the setpoint temperatures for operating the openings, the cooling setpoint of the HVAC and the limits for operative temperatures of the rooms were the most influent variables for the thermal and energy performance, especially in the heating period. The uncertainty was up to 65.6% for estimating the degree-hours for heating (in the natural ventilation mode) and up to 59.3% for estimating the total electricity consumption with HVAC (in the hybrid ventilation mode), indicating that these operational uncertainties had a great impact in the simulation results.

Cultural heritage plays an important role in society, not only in cultural terms but also due to its touristic interest. From a purely economic point-of-view, the increasing number of visitors can be a way to achieve financial sustainability. However, it is necessary to ensure that conservation and comfort conditions are not affected, since the human body releases heat, moisture, CO2 and odours.