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 Frederik Auffenberg

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Bibliometrics: publication history
Average citations per article0.60
Citation Count3
Publication count5
Publication years2015-2017
Available for download4
Average downloads per article74.00
Downloads (cumulative)296
Downloads (12 Months)252
Downloads (6 Weeks)46
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1 published by ACM
December 2017 ACM Transactions on Intelligent Systems and Technology (TIST): Volume 9 Issue 3, January 2018
Publisher: ACM
Citation Count: 0
Downloads (6 Weeks): 20,   Downloads (12 Months): 21,   Downloads (Overall): 21

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In this article, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation, and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set-point temperature. Being built using broad population statistics, ...
Keywords: agents, User behaviour modeling and learning, machine learning, computational sustainability

2 published by ACM
May 2017 CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
Publisher: ACM
Citation Count: 0
Downloads (6 Weeks): 26,   Downloads (12 Months): 218,   Downloads (Overall): 218

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Smart thermostats offer impressive scope for adapting to users' thermal comfort preferences and saving energy in shared work environments. Yet human interactions with smart thermostats thus far amount to an assumption from designers that users are willing and able to provide unbiased data at regular intervals; which may be unrealistic. ...
Keywords: office, shared work environments, smart thermostat, thermal comfort, participatory sensing, reciprocity

July 2016 HCI '16: Proceedings of the 30th International BCS Human Computer Interaction Conference: Companion Volume
Publisher: BCS Learning & Development Ltd.
Citation Count: 0
Downloads (6 Weeks): 0,   Downloads (12 Months): 0,   Downloads (Overall): 0

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This paper details our work towards designing a system for crowd-sourcing responses on thermal comfort in naturally ventilated office buildings. We provide preliminary qualitative findings on the deployment of this system. Specifically, we explore the different human factors that led to our system being used as something akin to a ...
Keywords: office, social factors, thermal comfort, natural ventilation

July 2015 IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence
Publisher: AAAI Press
Citation Count: 1

In this paper, we address the challenge of predicting optimal comfort temperatures of individual users of a smart heating system. At present, such systems use simple models of user comfort when deciding on a set point temperature. These models generally fail to adapt to an individual user's preferences, resulting in ...

May 2015 AAMAS '15: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems
Publisher: International Foundation for Autonomous Agents and Multiagent Systems
Citation Count: 1
Downloads (6 Weeks): 4,   Downloads (12 Months): 23,   Downloads (Overall): 52

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We present a novel, personalised thermal comfort model and a heating agent using this model to reduce energy consumption with minimal comfort loss. At present, heating agents typically use simple models of user comfort when deciding on a set point temperature for the heating or cooling system. These models however ...
Keywords: bayesian networks, agent-based control, thermal comfort, human-agent collectives, smart heating

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