Explore the vast range of titles available.

In order to achieve the goal of the Paris Agreement and reduce energy consumption there is the need for a behavior change in energy end-users. Many studies have demonstrated that by delivering to energy users customized feedback on their energy consumption it can encourage a change in their behavior and support investments in energy efficiency and sustainable energy use. However, the resulting impact on energy consumption can vary largely depending on how, when, and to whom the feedback is delivered. This paper aims to provide an updated overview of the energy savings for the main energy consumptions in residential buildings associated with different types of feedback and against some key determinants, i.e., geographical area, time period, type of medium. This analysis is based on a comprehensive literature review of over 70 studies. Based on the review the paper draws conclusions relevant for policymakers and stakeholders interested in developing feedback strategies and tools for their wide applications. The paper focuses also on the ongoing process implementing the EU Energy Efficiency Directive articles related to billing and metering, which will enable more proactive consumer feedback.

Buildings consume a significant amount of energy, estimated at about one-third of total primary energy resources. Building energy efficiency has turned out to be a major issue in limiting the increasing energy demands of the sector. Literature shows that building user behavior can increase the efficiency of the energy used in the building and different strategies have been tested to address and support this issue. These strategies often combine the quantification of energy savings and qualitative interpretation of occupant behavior in order to foster energy efficiency. Strategies that influence building occupant behaviors include eco-feedback, social interaction, and gamification. This review paper presents a study conducted on the state of the art related to the impact of building user behavior on energy efficiency, in order to provide the research community with a better understanding and up-to-date knowledge of energy, comfort-related practices, and potential research opportunities. Achieving and maintaining energy-efficient behavior without decreasing the comfort of building occupants still represents a challenge, despite emerging technologies and strategies as well as general research progress made over the last decade. Conclusions highlight eco-feedback as an effective way to influence behavior, and gamification as a new opportunity to trigger behavioral change. The impact of user behavior is difficult to quantify for methodological reasons. Factors influencing human behavior are numerous and varied. Multi-disciplinary approaches are needed to provide new insights into the inner dynamic nature of occupant’s energy behavior.

Building occupant presence during varying periods is crucial to the performance studies of buildings and city regions. However, the understanding of the building occupancies on the university campus remains limited. To address this gap, our study employs field measurements, payment records, course arrangements, and building access systems to depict the occupancy patterns of the canteen, dormitory, library, and teaching and lab buildings during weekdays and weekends. We found that the occupancy rates across different buildings are somehow interrelated, given that the total number of occupants on campus is generally constant. Notably, dormitory occupancy rates tend to be low during the morning and afternoon course hours, which inversely correlates with the high occupancy rates in the teaching and lab buildings during these periods. Similarly, canteens experience surges in occupancy during meal times, which coincide with a decrease in library usage. Moreover, we established appliance operation schedules for dormitories through surveys and on-site investigations. Water dispensers and electronic devices were identified as the primary energy consumers for both male and female occupants, with desk-top fans and hairdryers being significant energy users for male and female occupants, respectively. These findings are essential for energy studies within a campus setting, underlining the importance of considering occupant behaviors on a regional scale.

The goal of this literature review was to outline the research currently conducted on smart meter (SM) adoption and its connection to building occupant behavior to better understand both SM technology and SM customers. We compiled our findings from the existing literature and developed a holistic understanding of the socio-demographic factors that lead to more or less energy use, the methods used to group and cluster occupants on the basis of energy use, how occupant energy use profiles are developed, and which socio-psychological determinants may influence SM adoption. Our results highlight 11 demographic variables that impact building energy use, find 9 methods commonly used to profile occupants on the basis of energy usage, and highlight 13 socio-psychological variables than can be utilized to better understand SM adoption intentions. The review findings two major deficiencies in the existing literature. First, this review highlights the lack of existing interdisciplinary research that combines occupant behavior with SM data and a clear socio-psychological framework. Second, this review underscores certain data limitations in existing SM research, with most research being conducted only on residential or office buildings and geographically in North America or Western Europe. Final policy recommendations center on increased need for interdisciplinary SM research and the need for an expanded understanding of occupant behavior and SM research across different geographies.

This paper characterizes variations in the indoor temperature and humidity profiles of actual guest rooms equipped with Occupancy-Based Climate Control (OBCC) systems that were used to initiate a temperature setback to 15.6 °C in the winter and to 26.7 °C in the summer in the guest rooms. Empirical knowledge of these conditions can provide useful insights for an improved field demonstration and optimization of OBCC, as well as for a more realistic temperature and occupancy input for building simulations for hotel guest rooms. As a result, one year of one minute temperatures and humidity data was characterized against outdoor climate for three different occupancy modes, which was useful to identify the observed room-to-room variations in heat losses and resultant indoor temperatures during the heating season due to the different dynamic heat balance conditions of the guest rooms. This indicated potential discomfort in the rooms that appeared to have a stronger association between outdoor and indoor temperatures, which was also identified from the thermal comfort survey indicating thermostat-related discomfort sources. Interestingly enough, the guests who stayed in these rooms tended to set their thermostat at higher setpoint temperatures when they occupied the room, which appeared to compensate for the low balance-point temperatures of these rooms.

In the light of global climate change and the current energy crisis, it is crucial to target sustainable energy use in all sectors. Buildings still remain one of the most energy-demanding sectors. Campus buildings and higher educational buildings are important to target due to their high and increasing energy demand. This building segment also represents a research gap, as mostly office or domestic buildings have been studied previously. In the quest for thermal comfort, a key stakeholder in building energy demand is the building occupant. It is therefore crucial to promote energy-aware behaviors. The building systems are another key factor to consider. As conventional building systems are replaced with smart building systems, the entire scenario is redrawn for how building occupants interact with the building and its systems. This study argues that behavior is evolving with the smartness of building systems. By means of a semi-systematic literature review, this study presents key findings from peer-reviewed research that deal with building occupant behavior, building systems and energy use in campus buildings. The literature review was an iterative process based on six predefined research questions. Two key results are presented: a graph of reported energy-saving potentials and a conceptual framework to evaluate building occupants impact on building energy use. Furthermore, based on the identified research gaps in the selected literature, areas for future research are proposed.

Design strategies for sustainable buildings, that improve building performance and avoid extensive resource utilization, should also promote healthy indoor environments. The following paper contains a review of the couplings between (1) building design, (2) indoor environmental quality and (3) occupant behavior. The paper focuses on defining the limits of adaptation on the three aforementioned levels to ensure the energy efficiency of the whole system and healthy environments. The adaptation limits are described for measurable physical parameters and the relevant responsible human sensory systems, evaluating thermal comfort, visual comfort, indoor air quality and acoustical quality. The goal is to describe the interactions between the three levels where none is a passive participant, but rather an active agent of a wider human-built environment system. The conclusions are drawn in regard to the comfort of the occupant. The study reviews more than 300 sources, ranging from journals, books, conference proceedings, and reports complemented by a review of standards and directives.

Computational performance-driven design optimization (CPDDO) informs early building design decisions, enhancing projects’ responsiveness to local climates. This paper reviews recent CPDDO studies, identifies prevalent gaps, and proposes a refined optimization framework. The framework stands out by: (1) integrating view quality alongside energy, daylight, and thermal comfort considerations, with a vector-simulation-based metric considering content, access and clarity; (2) incorporating users’ adaptive behavior patterns in simulations; and (3) employing a hybrid weighting method to accommodate diverse project demands and support robust design decisions. This study applies the framework to optimize the shape and facade variables of a medium-sized office building in Guangzhou, Chongqing, Qingdao, Lanzhou, and Changchun, representing hot, warm, mixed, cool, and cold climates, respectively. Results highlight that geometry features (aspect ratio, orientation, window-to-wall ratio (WWR), and shading devices), as well as window and blinds constructions significantly impact energy, daylight, thermal comfort and view quality. Different climatic conditions, objective priorities, and facade orientations necessitate tailored design variables. Furthermore, certain findings challenge conventional recommendations; for instance, buildings in colder climates benefit from increased WWR, due to enhanced potential to harness solar radiation and improved view access, while high-performance envelope thermal settings mitigate heat transfer. These findings underscore the need for detailed, targeted research in early-stage design. The developed CPDDO framework proves effective and user-friendly, offering new possibilities for optimizing building performance, thus holds the potential to foster green, comfortable, and sustainable architecture in various practical applications.

The energy consumption of a building is significantly influenced by the habits of its occupants. These habits not only pertain to occupancy states, such as presence or absence, but also extend to more detailed aspects of occupant behavior. To accurately capture this information, it is essential to use tools that can monitor occupant habits without altering them. Invasive methods such as body sensors or cameras could potentially disrupt the natural habits of the occupants. In our study, we primarily focus on occupancy states as a representation of occupant habits. We have created a model based on artificial neural networks (ANNs) to ascertain the occupancy state of a building using environmental data such as CO2 concentration and noise level. These data are collected through non-intrusive sensors. Our approach involves rule-based a priori labeling and the use of a long short-term memory (LSTM) network for predictive purposes. The model is designed to predict four distinct states in a residential building. Although we lack data on actual occupancy states, the model has shown promising results with an overall prediction accuracy ranging between 78% and 92%.