Public educational systems operate thousands of buildings with vastly different characteristics in terms of size, age, location, construction, thermal behavior and user communities. Their strategic planning and sustainable operation is an extremely complex and requires quantitative evidence on the performance of buildings such as the interaction of indoor-outdoor environment. Internet of Things (IoT) deployments can provide the necessary data to evaluate, redesign and eventually improve the organizational and managerial measures. In this work a data mining approach is presented to analyze the sensor data collected over a period of 2 years from an IoT infrastructure deployed over 18 school buildings spread in Greece, Italy and Sweden. The real-world evaluation indicates that data mining on sensor data can provide critical insights to building managers and custodial staff about ways to lower a buildings energy footprint through effectively managing building operations.
2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Pages 278-283
On Mining IoT Data for Evaluating the Operation of Public Educational Buildings (04b Atto di convegno in volume)
Zhu Na, Anagnostopoulos Aristidis, Chatzigiannakis Ioannis
Gruppo di ricerca: Algorithms and Data Science