Abstract
Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. We consider a method for an automated agent to provide advice to drivers which will motivate them to reduce the energy consumption of their climate control unit. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system's settings, and the other, models human comfort level as a function of the climate control system's settings. Using these models, the agent provides advice to the driver considering how to set the climate control system. The agent advises settings which try to preserve a high level of comfort while consuming as little energy as possible. We empirically show that drivers equipped with our agent which provides them with advice significantly save energy as compared to drivers not equipped with our agent.
Original language | English |
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Title of host publication | 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 |
Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
Pages | 1391-1392 |
Number of pages | 2 |
ISBN (Electronic) | 9781634391313 |
State | Published - 2014 |
Event | 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 - Paris, France Duration: 5 May 2014 → 9 May 2014 |
Publication series
Name | 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 |
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Volume | 2 |
Conference
Conference | 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 |
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Country/Territory | France |
City | Paris |
Period | 5/05/14 → 9/05/14 |
Bibliographical note
Publisher Copyright:Copyright © 2014, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Keywords
- Human-agent interaction
- Persuasion
- Sustainability technologies