Data for: Recommender Systems for Sensor-based Ambient Control in Academic Facilities

Published: 5 June 2020| Version 1 | DOI: 10.17632/7j845nz5wh.1
Juan A. Gomez-Pulido


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. This dataset provides sensor readings from sensors over time in different academic rooms. The data are supplied in a file in Excel format .xlsx. It containts several sheets corresponding with the different smart spaces (laboratories and classrooms). Each dataset is a matrix where the rows are the time dates of the readings, and there are three columns for the sensor readings of temperature, CO2 and humidity.



Universidad de Extremadura


Artificial Intelligence, Machine Learning, Sensor, Smart Infrastructure