In a worldwide context, space heating is the largest energy consumer in commercial buildings, it accounts for 35% of the total energy consumed in the US. Energy efficient thermostats, that learn occupancy patterns and user preferences, haven been studied in literature. However, they are oriented to single-user environments, therefore, they are not applicable in offices where several users interact, i.e. multi-user environments. To expand the single-user techniques in order to cope with multi-user environments, two methods are proposed to derive the user's expected temperatures demands based on their occupancy profiles and individual preferences in terms of desired temperature and tolerance. This paper presents the implications of the implementation of such techniques by means of a case study of two users in an academic office. We observed that the proposed methods reduced the operational time up to 33% compared to a reference fixed schedule of 12 hours while maintaining user comfort. In conclusion, smart thermostats can also reduce energy consumption in multi-user environments while guaranteeing individual user expectations.

Probability estimation of machine usages is an essential task to the development of an intelligent device/environment. In this paper, we propose a generic framework to the task using the sliding window technique and incremental feature selection. The methodology is applied to a real-life dataset of office printers and the performances of different standard classifiers in supervised learning are compared. We conclude that Logistic Regression (LR) outperform other classifiers and is appropriate for the proposed framework. The use of Generic Bayesian Network (GBN) classifier is also promising, if combined with feature reduction methods.