# On Mining IoT Data for Evaluating the Operation of Public Educational Buildings

## TL;DR

This paper demonstrates how data mining IoT sensor data from multiple school buildings across Europe over two years can provide valuable insights for improving energy efficiency and operational management.

## Contribution

It introduces a data mining approach for analyzing IoT sensor data collected from diverse educational buildings to support sustainable operation and strategic planning.

## Key findings

- Data mining reveals patterns in energy consumption and indoor environment interaction.
- Insights enable targeted operational improvements to reduce energy footprint.
- Long-term sensor data analysis supports sustainable building management.

## 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 building's energy footprint through effectively managing building operations.