Recommender systems for sensor-based ambient control in academic facilities

Abstract

Academic spaces are an environment that promotes student performance not only because of the quality of its equipment, but also because of its ambient comfort conditions, which can be controlled by means of actuators that receive data from sensors. Something similar can be said about other environments, such as home, business, or industry environment. However, sensor devices can cause faults or inaccurate readings in a timely manner, affecting control mechanisms. The mutual relationship between ambient variables can be a source of knowledge to predict a variable in case a sensor fails. Moreover, the relationship between these variables and the occupation of spaces by students over time also contains an adequate knowledge of the context for prediction. In this article we propose to predict ambient variables by means of recommendation systems based on collaborative filtering, which are fed with data from sensors over time in different academic rooms. For this purpose, we applied two different algorithms: Probabilistic Matrix Factorization and Bayesian Non-negative Matrix Factorization. The accuracy of the algorithms when comparing actual and predicted values and the performance comparison between the two collaborative filtering implementations lead us to propose Probabilistic Matrix Factorization as a good approach for supporting ambient control systems.