Occupant Behavior In Buildings Research Articles

A SENSITIVITY ANALYSIS METHOD COMBINING DEMPSTER-SHAFER THEORY AND MACHINE LEARNING FOR ENERGY-SAVING EVALUATION OF BUILDING OCCUPANT BEHAVIOR

ABSTRACT For a very long time, the research of the sensitivity analysis of occupant behavior to energy assessment has been in the spotlight. The key element of the research is determining the exact probability of occupant behavior uncertainty. However, due to the specificity of occupant behavior, data on occupant behavior from different independent sources of information can differ significantly. This paper explores the use of Dempster-Shafer theory to the sensitivity analysis of energy evaluation of occupant behavior in buildings. The Dempster-Shafer theory is an imprecise probability theory that allows the system to create assumed confidence intervals based on interval values probability combined with knowledge of uncertainty factors from many different sources of information. The findings show that the data processing approach based on Dempster-Shafer theory provides effective and reliable information for evaluating energy related to human behavior in buildings. To begin with, the sensitivity analysis process might be accelerated by applying machine learning to process the data. Then, in order to ensure the accuracy of the simulation results, multiple learning methods can be used. Finally, in this paper, model parameters were chosen based on the specific circumstances as soon as the model had been built in order to effectively reduce costs related to operation and increase model accuracy. To establish the final results, the model is evaluated using global sensitivity analysis methods.

Approach of establishing a high-resolution shading occupant behavior model in the office building

Occupant behavior in buildings is becoming a critical factor in determining indoor comfort and energy consumption performance. The diversity and uncertainty of occupant behavior affect the implementation of occupant-centric building operations. However, existing shading behavior models do not account for various shading states (fully open, fully closed, or partially open, significantly deviating from real-world scenarios), accessibility of shading behavior (ASB), room configurations (single-person rooms and multi-person rooms), occupancy, and other factors, resulting in lower model accuracy. This study aims to address these limitations by proposing a high-resolution shading occupant behavior (HRSOB) model that integrates occupancy considerations, accessibility of shading behavior, and data mining techniques. This HRSOB model can classify various shading occupant behaviors and be integrated into the optimization controls of occupant-centric buildings. The utilization of Markov chain occupancy models significantly improves the accuracy of predicting stochastic shading behavior. The ASB classification method analyzes the features of the occupant shading adjustment behavior (hereafter referred to as shading behavior) and objectively reflects shading behavior patterns using spatial features, thereby enhancing the resolution of the shading occupant behavior model. Data-driven techniques, such as feature selection and soft voting, enable the analysis, training, and application of shading behavior data, and rigorous evaluation and validation of the HRSOB model are conducted. The results demonstrated a 10.92% improvement in the predictive performance of the HRSOB model compared with the typical shading occupant behavior (SOB) model.

Occupant-centric HVAC and window control: A reinforcement learning model for enhancing indoor thermal comfort and energy efficiency

Occupant behavior plays a crucial role in enhancing indoor thermal comfort and achieving energy efficiency by influencing the operational modes of Heating, Ventilation, and Air Conditioning (HVAC) systems as well as windows. However, accurately quantifying the impact of occupant behavior on the indoor environment presents significant challenges in practical applications. This study introduces an innovative approach by leveraging the ASHRAE Global Building Occupant Behavior Database and harnessing the power of XGBoost in conjunction with Deep Q Networks (DQN) to construct a reinforcement learning model. This model enables precise prediction of the impact of occupant behavior on the indoor environment at the next time step under varying indoor-outdoor conditions, simultaneously targeting the dual objectives of indoor thermal comfort and energy conservation. By applying the XGB-DQN model in sample rooms of four international cities with distinct features, the results demonstrate a significant increase in indoor thermal comfort duration by 24 %, accompanied by a 24.7 % decrease in air conditioning usage compared to baseline models and actual occupant data. This research represents a pioneering effort in applying reinforcement learning techniques to accurately predict occupant behavior's impact on indoor environments, offering valuable insights for intelligent building design and energy management.

Using Interaction Geography to Explore Building Occupant Behaviors in Virtual Reality: A Pilot Study

Using Interaction Geography to Explore Building Occupant Behaviors in Virtual Reality: A Pilot Study by Hoa Vo* , Peter Huesemann-Odom Welch School of Art and Design, College of the Arts, Georgia State University, Atlanta, 30303, USA * Author to whom correspondence should be addressed. Journal of Engineering Research and Sciences, Volume 1, Issue 11, Page

Dynamic clustering and modeling of temporal data subject to common regressive effects

Clustering is used in many applicative fields to organize data into a few groups. Motivated by behavioral extraction issues from urban data, this paper proposes a new clustering method to model clusters with dynamic profiles while considering common regressive effects. As maximum likelihood estimation is not suitable in this case, the parameters of the proposed model were estimated using variational approximation. The ability of the model to estimate parameters was evaluated using various simulated data and compared with two other models. The article also proposes an application of this model to the extraction of occupant behavior in buildings using a real open source indoor temperature database. The objective is to classify individual houses according to indoor temperature while estimating the effect of meteorological variables and class profiles that can be interpreted as occupancy behaviors.

Data-driven Occupancy Profiles for Apartment-style Student Housing

To support the need for a better understanding of occupant behaviors in buildings, we present occupancy profiles for apartment-style student housing from high-resolution monitored occupancy data. We illustrate differences between data-driven occupancy patterns and the widely-used reference occupancy schedule in Building America’s House Simulation Protocol. We evaluate the sensitivity of predicted savings associated with the installation of occupancy-based heating and cooling controls to differences in occupancy schedules. Results demonstrate a wide variety of occupancy patterns among students. Weekday occupancy schedules are influenced by students’ class schedules with the average probability that students are home mid-day twice that defined by the reference occupancy schedule. However, the differences between the reference and actual occupancy profiles have produced a small change (~3%) in the savings predicted for occupancy-based heating and cooling controls in a cold climate (4.3–7.7%). The potential savings was not sensitive to the variability among student occupancy schedules due to the high probability of occupancy for most hours of the day. Nonetheless, the variety of occupancy patterns among students reveals diversity in students’ behaviors. Better understanding of occupant behavior in buildings provides crucial information for demand and distribution management of electrical grids, behavioral studies, and occupant-centric building design.

On the short term forecasting of heat power for heating of building

The energy efficiency of existing buildings may be increased by using new control techniques of their heating systems, especially if such methods are validated and easy to install. Hence, short-term forecasting of heat power demand is needed, in order to optimize their operation. This work presents a simple, new method of short-term forecasting of heat power for space heating, which may be easily applied in existing buildings. The method is first presented and then validated with two case studies, a multifamily building and a school, using hourly data from three heating seasons. It was found that beyond the outdoor meteorological parameters the accuracy of the method is improved by including the equivalent indoor temperature as the parameter related to the effect of the building occupant behavior. Accordingly, the resulting mean absolute percentage error of the predicted heat demand using the proposed prediction method was 3.2% and 12.0% for the two buildings. Compared to a simple model of the heat power demand that is based only on the outdoor temperature error was lower by 61.4% and 43.2% for two buildings respectively. In addition, five profiles of equivalent indoor temperature were proposed in order to select the most accurate one for a specific building. This method may be also used in the process of predictive control of heating systems, because the external and internal parameters are measurable and predictable, which will contribute to more energy efficient systems in existing and new buildings.

Decoupling the heat loss coefficient of an in-use office building into its transmission and infiltration heat loss coefficients

The actual building energy performance essentially depends on the building occupant's behaviour, the real performance of the installed energy systems and the in-use performance of the building envelope. The thermal performance characterization of in-use building envelopes, based on monitored data, represents a crucial step towards bridging the gap between the designed and as-built energy performance of buildings. The main indicator to analyse the performance gap of building envelopes is the Heat Loss Coefficient (HLC); when measured, it commonly shows considerable differences when compared with the design value. This research goes further and proposes a method, based on monitored data from in-use buildings, for the decoupling of the HLC of in-use buildings into its transmission (UA) and infiltration (C v ) heat loss coefficients, in order to identify the origin of the heat losses. The identification of this origin will facilitate the reduction of the performance gap. Therefore, a multi-storey occupied office building of the University of the Basque Country has been monitored and analysed, where the in-use HLC for each floor and for the whole building have already been estimated using an average method. Then, based on the ASTM D6245-18 Standard, the decay method of the metabolic CO 2 of the building's occupants has been successfully applied in this paper to obtain the Air Change per Hour (ACH) rates due to infiltrations. These ACH values have been used to decouple the estimated HLC values into their transmission and infiltration parts. • Metabolic CO 2 concentration decay method for estimating C v value of in-use buildings. • Decoupling the HLC into transmission (UA) and infiltration (C v ) heat loss coefficients. • Identify the heat losses origin in order to understand the building performance gap.

How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review

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.

Reinforcement learning of occupant behavior model for cross-building transfer learning to various HVAC control systems

Occupant behavior plays an important role in the evaluation of building performance. However, many contextual factors, such as occupancy, mechanical system and interior design, have a significant impact on occupant behavior. Most previous studies have built data-driven behavior models, which have limited scalability and generalization capability. Our investigation built a policy-based reinforcement learning (RL) model for the behavior of adjusting the thermostat and clothing level. Occupant behavior was modelled as a Markov decision process (MDP). The action and state space in the MDP contained occupant behavior and various impact parameters. The goal of the occupant behavior was a more comfortable environment, and we modelled the reward for the adjustment action as the absolute difference in the thermal sensation vote (TSV) before and after the action. We used Q-learning to train the RL model in MATLAB and validated the model with collected data. After training, the model predicted the behavior of adjusting the thermostat set point with R2 from 0.75 to 0.8, and the mean absolute error (MAE) was less than 1.1 °C (2 °F) in an office building. This study also transferred the behavior knowledge of the RL model to other office buildings with different HVAC control systems. The transfer learning model predicted the occupant behavior with R2 from 0.73 to 0.8, and the MAE was less than 1.1 °C (2 °F) most of the time. Going from office buildings to residential buildings, the transfer learning model also had an R2 over 0.6. Therefore, the RL model combined with transfer learning was able to predict the building occupant behavior accurately with good scalability, and without the need for data collection.

A framework for estimating the energy-saving potential of occupant behaviour improvement

Energy-related occupant behaviour in buildings has demonstrated considerable energy-saving potential. However, the current modelling method of occupant behaviour does not give sufficient considerations on the implementation difficulty of behaviour and provide a holistic map from survey data to various behaviour models. This article proposes a holistic survey-and-simulation-based framework for estimating the energy-saving potential of occupant behaviour improvement. In the framework, seven typical categories of occupant behaviour models are identified based on the survey results. According to the implementation difficulty, the models are integrated into four behaviour styles (baseline, wasteful, moderate and austere) to represent different levels of energy-saving consciousness of occupants. Based on a case study with a nationwide survey in Singapore, there are remarkable energy savings potential if occupant behaviour is improved; the building energy consumption can be reduced by up to 9.5% with the moderate behaviour improvement, and up to 21.0% with the aggressive behaviour improvement. The simulation results accord well with the measured results within a reasonable range of deviation. The framework can be applied to estimate the energy-saving potential of occupant behaviour improvement in a building with affordable cost, and the findings can inform a behaviour improvement programme with effective and efficient measures.

Building Energy Management for Passive Cooling Based on Stochastic Occupants Behavior Evaluation

The common approach to model occupants behaviors in buildings is deterministic and consists of assumptions based on predefined fixed schedules or rules. In contrast with the deterministic models, stochastic and agent based (AB) models are the most powerful and suitable methods for modeling complex systems as the human behavior. In this paper, a co-simulation architecture is proposed with the aim of modeling the occupant behavior in buildings by a stochastic-AB approach and implementing an intelligent Building Energy Management System (BEMS). In particular, optimized control logics are designed for smart passive cooling by controlling natural ventilation and solar shading systems to guarantee the thermal comfort conditions and maintain energy performance. Moreover, the effects of occupant actions on indoor thermal comfort are also taken into account. This study shows how the integration of automation systems and passive techniques increases the potentialities of passive cooling in buildings, integrating or replacing the conventional efficiency strategies.

Examining energy saving behaviors in student dormitories using an expanded theory of planned behavior

Buildings contribute to over one third of global energy-related carbon emissions, on which occupant behavior attains a significant impact. Occupant behavior is influenced by both internal and external factors, such as psychological and social factors, equipment properties. However, there is a marked lack of knowledge about the internal factors that influence occupant behaviors in buildings, in particular student dormitories. This paper aims to investigate energy saving behaviors using student dormitories as the case from a social psychological perspective. First, a theoretical framework was proposed based on the Theory of Planned Behavior (TPB) integrated with a new variable “personal moral norm”, to identify significant indicators of energy saving behaviors. Second, a questionnaire was conducted with 290 students to obtain data for model fitting. Third, the proposed framework was tested using two-step Structural Equation Modeling. Results show that students’ energy saving intentions are positively related to their behaviors. Personal moral norm has the greatest impact on energy saving intention, followed by perceived behavioral control and attitude, while the impact of subjective norm is insignificant. The additional variable “personal moral norm” significantly increases the explanation power of the TPB model. Moreover, gender and thermal sensation moderate the impact of energy saving intention on behavior. Finally, energy saving intervention strategies are suggested for student dormitories. The findings contribute to a better understanding of energy use behaviors in student dormitories and add to the existing body of knowledge about social psychological indicators of energy saving behaviors by confirming the role of moral beliefs.

A Thematic Network-Based Methodology for the Research Trend Identification in Building Energy Management

The rapid increase in the number of online resources and academic articles has created great challenges for researchers and practitioners to efficiently grasp the status quo of building energy-related research. Rather than relying on manual inspections, advanced data analytics (such as text mining) can be used to enhance the efficiency and effectiveness in literature reviews. This article proposes a text mining-based approach for the automatic identification of major research trends in the field of building energy management. In total, 5712 articles (from 1972 to 2019) are analyzed. The word2vec model is used to optimize the latent Dirichlet allocation (LDA) results, and social networks are adopted to visualize the inter-topic relationships. The results are presented using the Gephi visualization platform. Based on inter-topic relevance and topic evolutions, in-depth analysis has been conducted to reveal research trends and hot topics in the field of building energy management. The research results indicate that heating, ventilation, and air conditioning (HVAC) is one of the most essential topics. The thermal environment, indoor illumination, and residential building occupant behaviors are important factors affecting building energy consumption. In addition, building energy-saving renovations, green buildings, and intelligent buildings are research hotspots, and potential future directions. The method developed in this article serves as an effective alternative for researchers and practitioners to extract useful insights from massive text data. It provides a prototype for the automatic identification of research trends based on text mining techniques.

The reinforcement learning method for occupant behavior in building control: A review

• Literatures of reinforcement learning regarding occupant behavior were presented. • We provided a comprehensive understanding of how the method works. • We discussed an overview of implementation requirements and challenges. • Future research directions surrounding building control were proposed. Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment. For complex and dynamic environments, the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable. It is expected that they will make a substantial contribution to reducing global energy consumption. Among these control techniques, the reinforcement learning (RL) method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control. Fruitful algorithms complement each other and guarantee the quality of the optimization. However, the examination of occupant behavior based on reinforcement learning methodologies is not well established. The way that occupant interacts with the RL agent is still unclear. This study briefly reviews the empirical applications using reinforcement learning, how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.

Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation

Despite the fact that buildings are designed for occupants in principle, evidence suggests buildings are often uncomfortable compared to the requirements of standards; difficult to control by occupants; and, operated inefficiently with regards to occupants’ preferences and presence. Meanwhile, practitioners –architects, engineers, technology companies, building managers and operators, and policymakers – lack the knowledge, tools, and precedent to design and operate buildings optimally considering the complex and diverse nature of occupants. Building on the success of IEA EBC Annex 66 (“Definition and simulation of occupant behavior in buildings”; 2013–2017), a follow-up IEA EBC Annex 79 (“Occupant-centric building design and operation”; 2018–2023) has been developed to address gaps in knowledge, practice, and technology. Annex 79 involves international researchers from diverse disciplines like engineering, architecture, computer science, psychology, and sociology. Annex 79 and this review paper have four main areas of focus: (1) multi-domain environmental exposure, building interfaces, and human behavior; (2) data-driven occupant modeling strategies and digital tools; (3) occupant-centric building design; and (4) occupant-centric building operation. The objective of this paper is to succinctly report on the leading research of the above topics and articulate the most pressing research needs – planned to be addressed by Annex 79 and beyond.

Modeling occupant behavior in buildings

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 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.

Using the Instance-Based Learning Paradigm to Model Energy-Relevant Occupant Behaviors in Buildings

Human interactive behavior is accountable for most of the variance between the observed and predicted energy consumption of buildings, and is accordingly acknowledged as a major field of research into limiting building-related energy consumption. A thorough understanding of occupant behavior is critical to facilitate a more reliable prediction of energy consumption and identifying means by which pro-environmental behaviors can be promoted. Insights and models from psychology and sociology appear to be best suited to improving such understanding, and this article contributes to this end by developing and testing a cognitive model that serves as the core of a numerical human-building interaction model. The proposed implementation builds on instance-based learning, a well-established cognitive modeling paradigm, is integrated into a thermodynamic building model, and complemented by perception models for the approximation of the thermal and olfactory perception of the environment. The model successfully learns to interact plausibly with a set of elements of a model room—a heating system, a window, and the actor’s clothing—in order to establish predefined room conditions. Accumulation of context-specific instances in the declarative memory, which are retrieved and blended in a decision situation, provide the model with the flexibility to adapt its actions to very different climatic contexts, represented by the locations Stuttgart, Madrid, Stockholm, and Melbourne. Moreover, the model manages to find appropriate compromises if need satisfaction requires contradictory actions, such as in situations where satisfaction of the olfactory need requires opening the window and satisfaction of the thermal need requires keeping it closed. Despite its obvious complexity, the model must be considered to be a basic model, which restricts the immediate comparability of its results to human behavior data. However, the successfully applied plausibility checks clearly indicate the value of the cognitive approach to modeling human-building interaction.

Systematic Integration of Energy-Optimal Buildings With District Networks

The residential sector accounts for a large share of worldwide energy consumption, yet is difficult to characterise, since consumption profiles depend on several factors from geographical location to individual building occupant behaviour. Given this difficulty, the fact that energy used in this sector is primarily derived from fossil fuels and the latest energy policies around the world (e.g., Europe 20-20-20), a method able to systematically integrate multi-energy networks and low carbon resources in urban systems is clearly required. This work proposes such a method, which uses process integration techniques and mixed integer linear programming to optimise energy systems at both the individual building and district levels. Parametric optimisation is applied as a systematic way to generate interesting solutions for all budgets (i.e., investment cost limits) and two approaches to temporal data treatment are evaluated: monthly average and hourly typical day resolution. The city center of Geneva is used as a first case study to compare the time resolutions and results highlight that implicit peak shaving occurs when data are reduced to monthly averages. Consequently, solutions reveal lower operating costs and higher self-sufficiency scenarios compared to using a finer resolution but with similar relative cost contributions. Therefore, monthly resolution is used for the second case study, the whole canton of Geneva, in the interest of reducing the data processing and computation time as a primary objective of the study is to discover the main cost contributors. The canton is used as a case study to analyse the penetration of low temperature, CO2-based, advanced fourth generation district energy networks with population density. The results reveal that only areas with a piping cost lower than 21.5 k/100 m2ERA connect to the low-temperature network in the intermediate scenarios, while all areas must connect to achieve the minimum operating cost result. Parallel coordinates are employed to better visualise the key performance indicators at canton and commune level together with the breakdown of energy (electricity and natural gas) imports/exports and investment cost to highlight the main contributors.

A systematic development and validation approach to a novel agent-based modeling of occupant behaviors in commercial buildings

Occupant behaviors are one of the dominant factors that influence building energy use. Traditional building energy modeling programs use typical occupant schedules that often do not reflect actual situations. Robust occupant behavior modeling that seamlessly integrates with building energy models will not only improve simulation performance, but also provide a deeper understanding of occupant behaviors in buildings. This paper presents a development and validation approach to a novel occupant behavior model in commercial buildings. A robust agent-based modeling (ABM) tool, namely Performance Moderator Functions server (PMFserv), is used as the basis of the occupant behavior model. The ABM considers various occupant perceptions and interactions with window, door, and window-blinds based on the environmental conditions. An elaborate agent-based model that represents an office space in an existing building is developed. This is followed by a validation study of the ABM through the use of embedded sensors that capture the indoor ambient conditions and a survey to record actual occupant behaviors. By comparing the recorded behavior data with ABM output, this paper discusses the proposed ABM's prediction ability, limitations, and extensibility. Finally, the paper concludes with the potential of integrating the occupant behavior model with building energy simulation programs.