Occupant Behavior Impact on Building Sustainability Performance: A Literature Review
Abstract
:1. Introduction
2. Methodology for the Literature Analysis of the Behavior of Buildings and Their Impact
- Only “Review” and “Research papers”;
- These articles must be written in English;
- These articles are available electronically;
- Articles must be from 2010 to 2020 (rise in interest in occupant behavior);
- Articles which are directly related to the objective of the review.
- Titles and keywords analysis—Selection of articles by reviewing their titles and keywords using search strings.
- Snowballing—Including additional documents based on checking references to previously selected documents. This process can be repeated many times as new documents are found; however, only the first repetition was applied in this work.
- By abstract selection—Using the abstract for adding and removing criteria for nominations to be designated for the next stage.
- Full-text selection—Full text of the candidate papers from the previous stage, using the adding and removing criteria for the final selection [16].
3. Co-Occurrence of Keywords
4. Overview of Occupant Behavior and Brief Review of Modeling Approaches
4.1. Deterministic Occupant Modeling and Limitation
4.2. Stochastic Occupant Behavior Modeling
4.3. Multi-Agent-Based Modeling
5. Parameters Influencing Energy-Related Occupant Behavior
6. Previous Related Research on the Impact of Occupant Behavior on Building Energy Performance
7. Building Performance Simulation and Limitations
8. Occupant Behavior Modeling and Building Performance Simulation: Toward Integrated Approaches
9. Discussion and Research Gaps
9.1. Limitations of Generic and Robust Occupant Behavior Model
- Occupant behavior is difficult to model due to the stochastic nature and variability of the occupants; it is necessary to explore the generic pattern of their behaviors and integrate the information into the building energy model. In other words, the environmental conditions in which an occupant resides will cause adaptive behavior, while energy consumption may, therefore, be misused. Therefore, a valid occupant behavior model should be able to simulate the actual users’ responses to the different environments.
9.2. Lack of Actual Data for the Validation
- In recent years, many researchers in this field have focused primarily on the impact of occupant behavior on the energy optimization of buildings. The main drawback of modeling the occupant behavior is the difficulty in showing the reality. The dynamics of the occupant not only interact with the building systems, but also individually reflect the changes in their surroundings to maintain their comfort. Coupling the behavior of stochastic occupants in building simulations significantly reduces the uncertainties of the real world. This cannot be ignored, because one of the main objectives of modeling occupant behavior is to reduce the gap between real and simulated energy consumption. However, almost all studies that applied the old model to simulate stochastic occupant behavior are not validated.
9.3. Lack of Research on Different Types of Buildings (Institutions, University Buildings)
- There is a relatively high level of research in the domestic sector. The review research, which studied the occupant behavior impact on building energy performance, has focused on residential and office buildings (40 and 33%, respectively) and very little research has analyzed commercial and educational (3 and 3%, respectively) buildings. The institutional and hospital building sectors (a total of around 3% of the review) are particularly neglected, and require further research due to their significant carbon emissions.
9.4. Limitations of Considering All Factors Which Influence Occupant Behavior
- The limitations of building dynamic simulations regarding occupant behaviors are well known and several studies aim to overcome such obstacles. Occupant behavior models have not yet been established and BPS tools do not enable the consideration of several fundamental variables which influence human behaviors, such as physiological, psychological and social factors [105]. Therefore, one of the main challenges nowadays is to be able to accurately simulate a building’s energy performance with current tools and to predict which share of this consumption is due to occupants’ behavior.
9.5. Considering the Limited Number of Occupant Behaviors
- According to the literature reviewed, different types of occupant interactions with building systems, such as adjusting lighting, thermostat setpoint, windows and shades, were investigated. However, some areas, such as the use of hot water radiators and domestic hot water (DHW), which have a significant impact on energy consumption in an office building, have received slight attention. In addition, future investigations of the relationship between the different characteristics of the occupants are necessary, which will lead to more realistic estimates simulating the energy of the building.
9.6. Limitations of Coupling of Occupant Behavior into Building Energy Simulations
- Many studies contain detailed methods, including case studies, experiments, field measurements, surveys and questionnaires, and simulations. The results clearly show a clear direction in understanding how occupant behavior affects the energy performance of buildings. However, the present results have significantly improved the estimation of the energy behavior of occupants in buildings. Combining the findings of these stochastic occupants with integrated energy performance simulations to reduce the energy gap between predicted and actual values remains a major research challenge in this area [99,106,107].
9.7. Missing the Detailed Realistic Situations of Occupant Behavior
- More recently, multi-agent simulations have been used to optimize the energy consumption of buildings. This environment simulation method provides a realistic method to model occupant behavior, which plays a major role in the building’s energy performance. However, in the current multi-agent approach, where agents are representing occupants’ properties, such as tracking a person’s movement in a given location and counting occupants, the level of detail is negligible [32,46,49,51]. Consequently, the MAS modeling approach to modeling occupant behavior is not new, but the extent to which it covers complex occupant behavior modeling is limited [10,56].
9.8. Integrating Building Information Modeling (BIM) into Building Energy Modeling
- The emergence of BIM provides an opportunity for building engineers, architects and designers to give a solution for building energy modeling limitations such as tedious model preparing, model inconsistency and cost implementations, and propels the modeling process into the digital world. The energy consumption of the building is quite high, therefore, simulation tools are used by the designer to construct an energy-efficient building and most of the analysis is conducted at the end of the construction-drawing design stage, which means once the stage of selecting appropriate material required for the building is already complete. However, the estimation of building energy consumption at the first two stages of building design (preliminary and conceptual) and the occupant behavior impact in post-occupancy have a huge impact on the building energy consumption, which helps the designer to make a decision related to selecting different suitable design models and understanding users that leads to an energy-efficient building. BIM integrating occupant behavior into building simulation tools is used by designers to improve overall energy performance and the automation capabilities. However, building energy performance research studies lack a digital BIM model coupled with simulation tools, and this leads to wrong decisions and requires future research [12,108,109].
10. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Internal Driving Forces | ||
---|---|---|
Occupant-related | Psychological | Expectations and needs of comfort, lifestyle and habits, environmental awareness. |
Social | Interaction with other individuals, family composition | |
Biological | Clothing, age, gender and health activity. |
Ref. | Year | Journal/ Conference | Occupant Behavior | Methodology | Building Type | Impact | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
Window | Light | Shade | Thermostat | DHW | Occu. | ||||||
[7] | 2017 | Journal | √ | x | x | x | x | x | Simulation | Residential | 90% change in heating and cooling |
[62] | 2019 | Journal | √ | √ | x | x | √ | √ | Simulation | Residential | 14% improvement in energy consumption |
[63] | 2017 | Journal | x | x | x | x | x | √ | Simulation | Office | - |
[64] | 2013 | Journal | √ | x | x | x | x | x | Measurement | Residential | - |
[65] | 2013 | Journal | x | x | x | x | x | √ | Simulation | Residential | Zero energy gap |
[66] | 2012 | Journal | x | √ | x | x | x | x | Simulation | Office | 50% increase in lighting energy |
[67] | 2020 | Journal | x | x | x | x | x | √ | Measurement | Residential | 32–60% improvement in energy efficiency |
[68] | 2018 | Journal | x | √ | √ | x | x | √ | Simulation | Residential | - |
[26] | 2013 | Journal | √ | x | √ | x | x | √ | Simulation | Commercial | - |
[69] | 2017 | Journal | √ | √ | x | x | x | x | Simulation | Residential | 2% energy difference |
[70] | 2018 | Journal | √ | x | x | √ | x | √ | Simulation | Office | - |
[14] | 2020 | Journal | √ | √ | x | √ | x | √ | Measurement/Simulation | - | - |
[71] | 2011 | Conference | x | x | x | x | x | √ | Experiment | Office | 17.8% measured energy reduction |
[72] | 2018 | Conference | x | x | x | x | x | √ | Measurement | Educational | |
[73] | 2013 | Journal | x | x | x | x | x | √ | Simulation | Office | 50% less energy |
[74] | 2018 | Conference | x | x | x | x | x | √ | Measurement | Office | |
[11] | 2011 | Journal | x | x | x | x | x | √ | multi-agent | - | 12% reduction in energy consumption |
[75] | 2018 | Journal | √ | √ | √ | √ | x | x | Simulation | Office/Residential | |
[4] | 2019 | Journal | x | √ | x | x | x | √ | Simulation | Residential | 22.9% average deviation from measurements to 1.7% |
[76] | 2019 | Conference | x | x | x | √ | x | √ | Simulation/ Measurement | Office | 25% and 15% energy use variation |
[77] | 2017 | Conference | x | x | x | x | x | √ | Simulation | Residential | |
[78] | 2018 | Conference | x | √ | x | x | x | √ | Measured | Office | Considerable change |
[79] | 2018 | Journal | √ | √ | x | x | x | x | Measurement/Simulation | - | 26–58% reduction in the discrepancy |
[5] | 2017 | Journal | x | x | x | x | x | √ | Measurement | Institutional | |
[80] | 2019 | Journal | x | x | x | x | x | √ | Agent-based | Residential | |
[81] | 2015 | Journal | √ | x | √ | √ | x | x | Simulation | Residential | |
[55] | 2012 | Journal | x | x | x | x | x | √ | Simulation | Residential | |
[82] | 2020 | Journal | x | x | x | x | x | √ | Simulation | Office | |
[83] | 2014 | Journal | x | x | x | x | x | √ | Simulation | Office | |
[75] | 2018 | Journal | √ | √ | √ | √ | x | x | Simulation | Office/Residential |
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Ebuy, H.T.; Bril El Haouzi, H.; Benelmir, R.; Pannequin, R. Occupant Behavior Impact on Building Sustainability Performance: A Literature Review. Sustainability 2023, 15, 2440. https://doi.org/10.3390/su15032440
Ebuy HT, Bril El Haouzi H, Benelmir R, Pannequin R. Occupant Behavior Impact on Building Sustainability Performance: A Literature Review. Sustainability. 2023; 15(3):2440. https://doi.org/10.3390/su15032440
Chicago/Turabian StyleEbuy, Habtamu Tkubet, Hind Bril El Haouzi, Riad Benelmir, and Remi Pannequin. 2023. "Occupant Behavior Impact on Building Sustainability Performance: A Literature Review" Sustainability 15, no. 3: 2440. https://doi.org/10.3390/su15032440
APA StyleEbuy, H. T., Bril El Haouzi, H., Benelmir, R., & Pannequin, R. (2023). Occupant Behavior Impact on Building Sustainability Performance: A Literature Review. Sustainability, 15(3), 2440. https://doi.org/10.3390/su15032440