On Mining IoT Data for Evaluating the Operation of Public Educational Buildings
This work addresses the need for quantitative evidence to improve the sustainable operation of public educational buildings, but it is incremental as it applies existing data mining methods to a new dataset.
The authors tackled the problem of evaluating public educational building performance by analyzing IoT sensor data from 18 school buildings across three countries over two years, resulting in insights that can help lower energy footprints through better operational management.
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 building's energy footprint through effectively managing building operations.