Power Management of Personal Computers based on User Behaviour

Brian Setz, Faris Nizamic, Alexander Lazovik, Marco Aiello

2016

Abstract

It has been shown that up to 64 percent of personal computers in office buildings are left running during after-hours. Enabling power management options such as sleep mode is a straightforward method to reduce the energy consumption of computers. However, choosing the right timeout can be challenging. A sleep timeout which is too low leads to discomfort, whereas a timeout which is too high results in poor energy saving efficiency. Having the users choose their own sleep timeout is not viable as research shows that most users disable the sleep timeout completely, or choose a suboptimal timeout. Unlike existing context based power management systems which use predefined rules, we propose a solution which can determine a personalized sleep timeout for any point in time solely based on the users behaviour. We propose multiple models which have the goal of maximizing the energy savings while minimizing discomfort. The models are tested on the computers of employees of the University of Groningen over several weeks. We analyse the results of the experiments and determine which model performs best. We can potentially save between 4.02 and 17.17 kWh per computer per year, depending on the model that used.

Paper Citation

in Harvard Style

Setz B., Nizamic F., Lazovik A. and Aiello M. (2016). Power Management of Personal Computers based on User Behaviour . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 409-416. DOI: 10.5220/0005762604090416

in Bibtex Style

@conference{smartgreens16,
author={Brian Setz and Faris Nizamic and Alexander Lazovik and Marco Aiello},
title={Power Management of Personal Computers based on User Behaviour},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={409-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005762604090416},
isbn={978-989-758-184-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Power Management of Personal Computers based on User Behaviour
SN - 978-989-758-184-7
AU - Setz B.
AU - Nizamic F.
AU - Lazovik A.
AU - Aiello M.
PY - 2016
SP - 409
EP - 416
DO - 10.5220/0005762604090416