Occupant behavior in buildings has been a matter of concern all over the world. Buildings are responsible for a significant portion of energy consumption; therefore, improving the thermal and energy performance of such buildings requires knowledge about the variables that influence them. However, to increase the potential for improving thermal and energy performance of buildings, studies must also consider the occupant’s interactions with the built environment. The occupant behaviour influences the conditions of the internal environment through the occupation of the spaces and through the interaction with building elements, such as air-conditioning, lighting, blinds and windows. Thus, the objective of Occupant Behavior in Buildings: Advances and Challenges is to put together some of these aspects, presenting advances and challenges, by means of eight chapters written by renowned researchers.
Due to recent technological innovations related to Information and Communication Technologies (ICTs), buildings are undergoing some evolutions and incorporating technologies that endow them with intelligence. However, the requirement of building intelligence to be related to the response of the occupants’ needs leads to the consideration of the buildings as Cyber-Physical-Social Systems (CPSS). Combining technical and social dimensions in this new generation of buildings, occupants’ satisfaction and energy use can be improved. Mateus V. Bavaresco, Ricardo F. Rupp and Enedir Ghisi concluded that by enriching data collection and presentation, more professionals can access previous outcomes and adapt their practices towards achieving comfortable and energy-efficient buildings.
Occupancy is a paramount factor to achieve energy efficiency. The authors António Ruano, Karol Bot and Maria Ruano, proposed a new methodology to estimate the occupancy and analysed the impacts of occupants on thermal comfort and energy efficiency in buildings from two distinct sectors: residential and educational.
The knowledge of occupant actions and needs is determinant for the proper function of an intelligent building. Therefore, the building management systems (BMS) must be supplied with data from the occupants. However the data by itself does not ensure the knowledge of occupants’ needs and the ability to predict their behaviors. To do that, BMSs must be gifted with artificial intelligence (AI) and machine learning (ML) techniques to data mine the information provided by the monitoring systems. Pedro F. Pereira and Nuno M. M. Ramos compared methodologies used to detect occupant actions and occupants’ needs in the same case study. The compared methodologies have the ability of self-learning and, therefore, can the used in multiple circumstances.
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. Aline Schaefer, João Vitor Eccel and Enedir Ghisi studied the dependency relationship between the room’s occupancy schedule and the operation of openings in low-income houses in Florianópolis, southern Brazil. 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 gap between the estimate and actual thermal and energy performance is directly and indirectly attributed to occupants. To address such issue, Arthur Santos Silva investigated the uncertainties of occupant behaviour in building performance simulation through a probabilistic approach. The author 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 Heating, Ventilation and Air-Conditioning system (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 on the simulation results.
Cultural heritage plays an important role in society, not only in cultural terms but also due to its touristic interest. However, it is necessary to ensure that conservation and comfort conditions are not affected, since the human body releases heat, moisture, CO2 and odours. Hugo Entradas Silva and Fernando M. A. Henriques analysed the impact of the binomial ventilation vs. occupancy, simulating various combinations of ventilation and air recirculation on the indoor air quality, conservation and energy consumption in museums. Since the visits to major national museums take usually long periods, the concept of adaptation was analysed to reduce the airflow of fresh air per visitor.
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About the Editors:
Enedir Ghisi
Prof. Enedir Ghisi obtained his PhD in civil engineering from the University of Leeds, UK, in 2002. He is currently a professor at the Federal University of Santa Catarina, located in Florianópolis, southern Brazil. So far, he has published 11 book chapters, 140 conference articles, and 134 journal articles. He has supervised 11 Ph.D. and 30 master's students. Currently, he supervises 8 Ph.D. and 8 master's students. In 2020, he was recognised as one of the 100,000 most influential scientists in the world for the whole career and also for 2019.
Ricardo Forgiarini Rupp
Ricardo Forgiarini Rupp is a postdoc (Marie-Curie Fellow and H.C. Ørsted Fellow) at the International Centre for Indoor Environment and Energy, Technical University of Denmark (DTU Civil Engineering). His key research topics include indoor environmental quality, occupant’s comfort and behaviour, mixed-mode ventilation, energy efficiency, and sustainability in buildings. His research covers mainly the interaction between the indoor environment and human comfort, health, and performance, with a particular interest in investigating contextual and individual differences (e.g. gender). Dr. Rupp has co-supervised PhD, master’s and bachelor’s students, and he has been acting as a reviewer for several high-quality journals such as Building and Environment and Energy and Buildings. Ricardo has a PhD in civil engineering from the Federal University of Santa Catarina (Brazil) with a research stay at the Indoor Environmental Quality Laboratory, The University of Sydney, Australia. He is a civil engineer, also holding a master’s degree in civil engineering.
Pedro F. Pereira
Pedro F. Pereira finished his PhD in 2018 in the Faculty of Engineering, University of Porto. The PhD thesis was nationally recognized with the annual award of UPORTO to the best civil engineering PhD thesis. He has participated in 4 national R&D projects and 1 international R&D project. He served as an assistant professor at the Polytechnic Institute of Viana do Castelo for three years. He was the co-supervisor of seven MSc students. He also worked for four years as a consultant, having participated in over 30 technical reports. He has published 17 papers in international peer-review Q1/Q2 journals. He is the first author of one international book and author of two international book chapters. He has a SCOPUS h-index of 9. He has also published in 15 national and international conferences and was the speaker of seven oral presentations. He is currently a member of the editorial board of three international journals.
Keywords:
Occupant behavior, Building systems, Indoor environment, Drivers, Energy, Intelligent buildings, Internet of things, Performance, Energy efficiency, Building simulation, Indoor air quality, Building control Ventilation, Building operation, Occupancy, Behavioral sensing, Behavioral modelling, Heating, Cluster analysis
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