Ten questions concerning agent-based modeling of occupant behavior for energy and environmental performance of buildings

Malik, Jeetika; Mahdavi, Ardeshir; Azar, Elie; Putra, Handi Chandra; Berger, Christiane; Andrews, Clinton J.; Hong, Tianzhen

doi:10.1016/j.buildenv.2022.109016

Cited by 31 publications

(6 citation statements)

References 101 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current study identifies behaviour triggers and quantifies occupant behaviours within the scope of occupant behaviour modelling [5]. The information enabled in the decision path algorithms created in the current study can be linked to future occupant behaviour models and BPS tools for specific behaviours [12]. Furthermore, Tables A1 to A7 in the Appendix present the previous research on each behaviour and summarise the factors they considered influencing each behaviour.…”

Section: Scope Of Analysismentioning

confidence: 99%

“…Different types of occupant behaviour models are being developed for use in BPS, including schedules, profiles or rule-based, deterministic or statistical, non-probabilistic or data-based, probabilistic or stochastic, agent-based or object-oriented, and virtual models [10]. Given the stochastic manner of occupant behaviours, data-driven behavioural models based on non-probabilistic (i.e., data mining, machine learning), probabilistic (i.e., Markov chain, logit analysis, survival analysis), and agent-based modelling are widely known for their ability to provide valid, practical, and easy to understand behaviour patterns [11,12]. Recently, studies have focused on moving from deterministic schedules or rule-based BPS models [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Occupants’ Decision-Making of Their Energy Behaviours in Office Environments: A Case of New Zealand

2023

View full text Add to dashboard Cite

Understanding how occupants behave and interact with building systems is vital to energy efficiency in buildings. The building occupants’ behaviours are complex and influenced by diverse factors. A deep understanding of the underlying environmental, contextual, social, and psychological factors is the first step of many in establishing the relationship between the indoor environment and occupants’ behaviours. The current study investigates the influence of occupants’ perceived indoor environmental comfort, the availability of control, and the social-psychological impacts on occupant behaviours in a New Zealand context. The data were collected through online surveys, and 99 office occupants responded. A machine learning technique was applied to identify the critical factors influencing the decision-making of occupant behaviours. Of the occupant behaviours considered in the study, adjusting windows, doors, shades and blinds, and drinking beverages were mostly practised (>70%) while adjusting lighting, personal fans, thermostats/heaters, and computers (40−70%) was moderately practised by occupants. The availability of specific user controls was the main predictor of most occupant behaviours, followed by social-psychological factors such as actual knowledge, perceived knowledge, behavioural interventions, subjective norms, organisational support, personal norms, attitudes, and perceived behavioural control. The indoor environmental parameters such as indoor temperature, indoor air quality, natural light, and inside noise were highlighted as most influential in decision-making for occupant behaviours. Additionally, the demographic factors: gender, work duration, days at work, and permanence/temporariness of workspace, were also impactful. Knowing the complexity of occupants’ decision-making with respect to their behaviours helps building managers use this sensitive information to enhance building energy performance and enable more energy feedback to the occupants to raise their awareness. Such information is helpful for creating an intelligent environmental control system loop with eco-feedback and establishing occupant-centric buildings or features.

show abstract

Section: Scope Of Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Occupants’ Decision-Making of Their Energy Behaviours in Office Environments: A Case of New Zealand

2023

View full text Add to dashboard Cite

show abstract

“…In the resource-based view, resources and capabilities are important factors for a company to obtain excess profits and sustainable competitive advantages. Therefore, a company will make corresponding strategic choices in investment, technology, environment and other aspects [36][37][38][39]. Enterprise size is an important index to measure the competitive advantage of enterprises, which is directly related to their strategic decision, leading to the integration and allocation of resources and to the selection of specific low-carbon policies [40].…”

Section: Literature Reviewmentioning

confidence: 99%

Study on the Interaction Effect between the Intensity of Government Low-Carbon Subsidies and the Growth Ability of Green and Low-Carbon Emerging Enterprises

Chen

Huang

2023

IJERPH

View full text Add to dashboard Cite

With the development of science and technology and society, people’s demand for a healthy living environment is increasing, and the expression “low carbon” has become a daily feature of people’s lives. The emergence of a low-carbon economy, the impact on the traditional industrial structure and the formation of a new economic landscape make China, a developing country, eager to seize this opportunity to enhance its international competitiveness. To achieve this, it is necessary to establish a low-carbon concept, to actively restructure industrial and develop low-carbon industries; only in this way can we take advantage of the new round of industrial restructuring and grasp the initiative of development. Therefore, this paper selects data from enterprises in the emerging low-carbon industry, and uses a SVAR model to conduct a dynamic interaction analysis between government subsidy intensity, enterprise profitability, asset growth capacity and enterprise size. The results of the study show that the intensity of government subsidies in the first period has a certain positive effect on a company’s current profitability and asset growth, the improvement being most significant on its profitability. Among the larger companies, asset growth and profitability in the first period had a significant positive impact on current earnings, and the contribution of profitability to the company’s own performance was much larger than the average; among the smaller companies, asset growth and profitability in the first period had a significant positive impact on current earnings, and the contribution of asset growth to the company’s own performance was much larger than the average. The intensity of prior government subsidies in the Highs group has a significant positive relationship with the intensity of current government subsidies, the firm’s asset growth capacity and profitability; the intensity of prior government subsidies in the Lows group only has a significant positive impact on the asset growth capacity in the current period. In addition, the interaction between the three core variables in the Highs group is also significantly higher than that in the Lows group. This indicates that the government should implement differential policies and financial subsidies according to the actual needs of enterprises to maximize the effect of capital use and promote the development and growth of emerging enterprises.

show abstract

“…It is estimated that 80% of the current building stock will still be in use in 2050 and current renovations rates of 1% is too low to meet the Green Deal goals [1]. Advances in heating, ventilation, and air conditioning (HVAC) systems, energy-efficiency strategies for the building stock have been advocated to reduce energy consumption in the building sector and encouraging environmental friendly end-use behaviours [2]. These involve improved measures on the envelopes of buildings [3], such as employing innovative materials [4], advanced insulation and building structures [5], new construction and the renovation of existing buildings [6].…”

Section: Introductionmentioning

confidence: 99%

Simulation Model for the Evaluation of the Effect of Office Dressing Code on Building Space Cooling Demand

Vicidomini

Calise²,

Cappiello³

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

View full text Add to dashboard Cite

The indoor air temperature in buildings is one of the main parameters determining both the indoor thermal comfort of inhabitants and the energy consumption of the Heating, Ventilation and Air Conditioning systems (HVAC). Clothing insulation of building residents is a key factor dramatically affecting people thermal comfort and HVAC energy demand. Indoor set point temperature, depends on clothing factor, relative humidity and mean radiant temperature. Although the clothing insulation values are selected according to well-known standards, clothing insulation depends on several factors: the metabolic heat production, activity and gender (females tend to be cooler than males in cool conditions). Therefore, users can adjust their clothing insulation according to the outdoor temperature change, suiting their own thermal comfort requirement. In this framework, a dynamic simulation model for the evaluation of the comfort conditions and the cooling energy consumption based on the variation in clothing insulation for office applications is developed in the present work. In order to calculate the space cooling demand, a suitable thermal zone was modelled by the TRNSYS Type 56 coupled to the Google SketchUp TRNSYS3d plug-in. The model was validated and applied to a suitable case study, an office room located at University of Federico II in Naples (Italy). Different sensitivity analyses were performed changing the clothing insulation and the office set-point temperature, to estimate both the comfort conditions and cooling energy demands. The model can be considered a flexible tool to suggest simple clothing adjustment behaviors which may represent a tradeoff among thermal comfort, energy saving and dressing code.

show abstract

Ten questions concerning agent-based modeling of occupant behavior for energy and environmental performance of buildings

Cited by 31 publications

References 101 publications

Occupants’ Decision-Making of Their Energy Behaviours in Office Environments: A Case of New Zealand

Occupants’ Decision-Making of Their Energy Behaviours in Office Environments: A Case of New Zealand

Study on the Interaction Effect between the Intensity of Government Low-Carbon Subsidies and the Growth Ability of Green and Low-Carbon Emerging Enterprises

Simulation Model for the Evaluation of the Effect of Office Dressing Code on Building Space Cooling Demand

Contact Info

Product

Resources

About