An agent-based model to evaluate smart homes sustainability potential

Walzberg, Julien; Dandres, Thomas; Samson, Réjean; Merveille, Nicolas; Cheriet, Mohamed

doi:10.1109/pimrc.2017.8292682

Cited by 10 publications

(27 citation statements)

References 28 publications

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“…These agents only react to actions from occupants to change their on/off state in Abdallah, Basurra and Gaber (2018)'s work. In Walzberg et al (2018)'s article, electric appliances are described as intelligent agents able to share energy consumption feedback to the occupants and optimise their load time. In (Evora et al, 2011;Hauser, 2013), occupants are modelled at the household level.…”

Section: Literature Review On Existing Abms For Occupants' Behaviour mentioning

confidence: 99%

Li-BIM, an agent-based approach to simulate occupant-building interaction from the Building-Information Modelling

Micolier

Taillandier

Taillandier³

et al. 2019

Engineering Applications of Artificial Intelligence

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Building design involves many challenges and requires to take into account the interaction between the building and the users. Different occupant behaviour models implemented with building simulation tools (thermal, air quality, lighting) have been proposed. Among these, models based on the agent approach seem to be the most promising. However, existing models poorly describe human cognition and the social dimension. Moreover, they are often oriented towards a specific use (thermal simulation, waste management) without being transposable to another field, and they require a significant instantiation effort for each new case, making their use difficult. This article proposes an agent-based model called Li-BIM that simulates the behaviour of the occupants in a building and their indoor comfort. Li-BIM model is structured around the numerical modelling of the building-BIM-(with standard exchange format IFC), a highresolution cognitive model, and the coupling with various physical models. Li-BIM simulates the reactive, deliberative and social behaviour of occupants in residential dwellings based on the Belief-Desire-Intention architecture. This model, thanks its ease of use and flexibility, is an operational and relevant tool to support building design process with a human-centred approach. An application of the model is presented, focusing on energy consumption and the inhabitants' comfort. In-situ data obtained from the instrumented house that served as case study have been compared with simulation results from Li-BIM and a standard energy simulation software, demonstrating the reliability of the proposed model.

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Section: Literature Review On Existing Abms For Occupants' Behaviour mentioning

confidence: 99%

Li-BIM, an agent-based approach to simulate occupant-building interaction from the Building-Information Modelling

Micolier

Taillandier

Taillandier³

et al. 2019

Engineering Applications of Artificial Intelligence

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“…Three studies model rebound effects (Xu et al, 2009;Hicks, Theis and Zellner, 2015a;Walzberg et al, 2018).…”

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confidence: 99%

“…Regarding the life cycle steps considered, one-third of the studies only consider the use phase of the product system, either because previous LCAs have shown that the use phase of the product system is the most impactful phase Onat et al, 2017;Vasconcelos et al, 2017), or because the study aims to quantify the environmental impacts that are associated with the heterogeneity of the consumption/usage pattern (Hicks, Theis and Zellner, 2015b; Raihanian Mashhadi and Behdad, 2017;Walzberg et al, 2018).…”

mentioning

confidence: 99%

“…Half of the corpus uses a consequential modelling methodology, among which two studies specify that both attributional and consequential methodologies can be used (Raihanian Miller et al 2013), and seven studies do not specify the modelling methodology (Xu et al, 2009;Navarrete Gutierrez et al, 2015a;Querini and Benetto, 2015;Bustos-Turu et al, 2016;Lu and Hsu, 2017;Vasconcelos et al, 2017;Walzberg et al, 2018). We classified their modelling methodology as consequential, since ABM is used to investigate the change of demand for products that are not in the system boundary of the product system under study.…”

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confidence: 99%

“…ABM is used to assess direct consequences on the foreground consumption system. Indirect changes in the activities all along the supply chain of the product system (the response of the production system to the consumption demand) that can be affected by 20 the displacement effect are not considered, except from the research of Walzberg et al (2018), which includes an indirect rebound effect.…”

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confidence: 99%

See 2 more Smart Citations

To what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review

Micolier

Loubet

Taillandier

et al. 2019

Journal of Cleaner Production

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Life cycle assessment (LCA) has proven its worth in modelling the entire value chain associated with the production of goods and services. However, modelling the consumption system, such as the use phase of a product, remains challenging due to uncertainties in the socioeconomic context. Agent-based models (ABMs) can reduce these uncertainties by improving the consumption system modelling in LCA. So far, no systematic study is available on how ABM can contribute towards a behavior-driven modelling in LCA. This paper aims at filing this gap by reviewing all papers coupling both tools. A focus is carried out on 18 case studies which are analysed according to criteria derived from the four phases of LCA international standards. Criteria specific to agent-based models and the coupling of both tools, such as the type and degree of coupling, have also been selected. The results show that ABMs have been coupled to LCA in order to model foreground systems with too many uncertainties arising from a behaviour-driven use phase, local variabilities, emerging technologies, to explore scenarios and to support consequential modelling. Foreground inventory data have been mainly collected from ABM at the use phase. From this review, we identified the potential benefits from ABM at each LCA phase: (i) scenario exploration, (ii) foreground inventory data collection, (iii) temporal and/or spatial dynamics simulation, and (iv) data interpretation and communication. Besides, methodological guidance is provided on how to choose the type and degree of coupling during the goal and scope phase. Finally, challenging LCA areas of research that could benefit from the agent-based approach to include behaviour-driven dynamics at the inventory and impact assessment phase have been identified.

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Agent‐based negotiation approach for feature interactions in smart home systems using calculus of the context‐aware ambient

Alfakeeh

Al-Bayatti

Siewe

et al. 2019

Trans Emerging Tel Tech

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Smart Home Systems (SHSs) provide several services which are tailored to different residents' preferences. As a result, SHSs are highly exposed to undesirable interactions, known as feature interactions (FIs). FIs might occur as a result of a conflict in services' goals or a conflict with residents' preferences. Previous studies have proposed solutions based on applying priorities, in which some services or preferable features are disabled in favour of other services. Alternatively, the agent-based negotiation approach (ABNA) utilises agents and applies negotiation, enabling services with contrary features to work simultaneously. ABNA avoids applying priority between services or house residents' preferences whenever a space for a compromise exists. The mechanism of ABNA is based on the use of a hierarchy of features based on their contribution to the function of the service or on the importance of these features to house residents. To achieve a compromise between conflicting services, ABNA models services and residents by using agents, and implements a negotiation algorithm that allows services with conflicting features to work simultaneously. This paper presents a description of ABNA with a formal specification of ABNA in the Calculus of Context-aware Ambient (CCA). This enables the formal analysis of ABNA by using the execution environment of CCA.

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An agent-based model to evaluate smart homes sustainability potential

Cited by 10 publications

References 28 publications

Li-BIM, an agent-based approach to simulate occupant-building interaction from the Building-Information Modelling

Li-BIM, an agent-based approach to simulate occupant-building interaction from the Building-Information Modelling

To what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review

Agent‐based negotiation approach for feature interactions in smart home systems using calculus of the context‐aware ambient

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