Agent-Based Control of Thermostatic Appliances

Engler, Joseph; Kusiak, Andrew

doi:10.1109/green.2010.5453799

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Reviews of open innovation literature (Bogers et al, 2017;Hossain et al, 2016;Kovacs et al, 2015) further emphasise that constituents of knowledge transfer success in ecosystem contexts remain unexplored. Moreover, existing research commonly examines ecosystems at macro-levels of analysis (Adner & Kapoor, 2010;Engler & Kusiak, 2011;Rohrbeck et al, 2009). Information exchange supports the ecosystem infrastructure (Kannisto et al, 2020).…”

Section: Existing Literaturementioning

confidence: 99%

On the Combinatory Nature of Knowledge Transfer Conditions: A Mixed Method Assessment

2021

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Organisations are increasingly creating inter-organisational ecosystem partnerships to innovate openly. Despite effective knowledge management significantly supporting ecosystem infrastructures, empirical insights into the importance of and interdependencies between conditions for successful knowledge exchange across ecosystem contexts remain unexplored within existing literature. This study implements a mixed-method approach to ascertain which conditions are responsible for knowledge transfer success across innovation ecosystems. Interpretive Structural Modelling was employed to analyse questionnaires with key ecosystem stakeholders, in order to impose a hierarchical structure upon the conditions. The configurational nature of these conditions, and their combinations into solutions for success was ascertained through analysing semi-structured interviews using fuzzy-set Qualitative Comparative Analysis. Results reveal multiple, mutually exclusive pathways to knowledge transfer success, grouped into three solution types, increasing understanding of the interrelated nature of the knowledge transfer conditions. Limitations and implications for future research are provided.

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Section: Existing Literaturementioning

confidence: 99%

On the Combinatory Nature of Knowledge Transfer Conditions: A Mixed Method Assessment

2021

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“…In smart homes, the approach has also been used for the anticipatory and reactive control of HVAC and lighting [21]. Likewise, an agent-based control system was used for the optimization of a simulated residential water heating system [18]. The prediction of the mobility patterns and device usage of inhabitants has been done in the MAVHome project in order to satisfy the trade-off between cost and comfort [19].…”

Section: Agent-based Approaches For Energy Managementmentioning

confidence: 99%

Simulating Occupants' Behavior for Energy Waste Reduction in Dwellings: A Multiagent Methodology

Kashif

Dugdale

Ploix

2013

Advs. Complex Syst.

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Energy waste due to inhabitants' behavior in residential buildings has emerged as a potential research area due to the increasing worldwide population and growing energy needs. However, existing approaches for simulating energy consumption are mainly limited to office buildings and are based on static profiles. In this paper we propose a 4-step co-simulation methodology to assess how inhabitants' interactions with household appliances affect energy consumption. The approach is validated using a case study showing how human activities influence the energy consumption patterns of a refrigerator. The fridge was specifically chosen because it is a high energy-consuming appliance that is strongly affected by inhabitants' behaviors. In addition, modeling the fridge is nontrivial, and in choosing this appliance we show that it is possible to apply the approach to less complex appliances. A co-simulation approach is adopted with the fridge being physically modeled in Matlab and with human behavior being modeled in the Brahms language and simulation environment. The consumption distribution from the simulated scenario is compared with the actual distribution (using data from a consumption database), to find optimum values of tuning parameters with less than 10% variation. 1350022-1A. Kashif, J. Dugdale and S. Ploix This methodology enables us to simulate how human behaviors affect energy appliance consumption.Keywords: Energy waste reduction; agent based dynamic behavior simulations; behavior influenced appliance consumption modeling. 1350022-3A. Kashif, J. Dugdale and S. Ploix

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A Modern Approach to Include Representative Behaviour Models in Energy Simulations

Kashif

Ploix

Dugdale

2012

Towards Energy Smart Homes

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Agent-Based Control of Thermostatic Appliances

Cited by 3 publications

References 2 publications

On the Combinatory Nature of Knowledge Transfer Conditions: A Mixed Method Assessment

On the Combinatory Nature of Knowledge Transfer Conditions: A Mixed Method Assessment

Simulating Occupants' Behavior for Energy Waste Reduction in Dwellings: A Multiagent Methodology

A Modern Approach to Include Representative Behaviour Models in Energy Simulations

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