Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | 2018 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS) | Computer science,Internet of Things,Architectural engineering,Atmospheric measurements,Educational systems,Footprint,Strategic planning,Energy consumption,Distributed computing,Cloud computing |
DocType | ISSN | Citations |
Conference | 2474-2503 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Na Zhu | 1 | 0 | 0.34 |
| Aris Anagnostopoulos | 2 | 1054 | 67.08 |
| Ioannis Chatzigiannakis | 3 | 1238 | 121.01 |