Power management of personal computers based on user behaviour

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.