Miia Lillstrang, Markus Harju, Guillermo del Campo, Gonzalo Calderon, Juha Röning, Satu Tamminen, Implications of properties and quality of indoor sensor data for building machine learning applications: Two case studies in smart campuses, Building and Environment, Volume 207, Part B, 2022, 108529, ISSN 0360-1323, https://doi.org/10.1016/j.buildenv.2021.108529
Implications of properties and quality of indoor sensor data for building machine learning applications : two case studies in smart campuses
|Author:||Lillstrang, Miia1; Harju, Markus1; del Campo, Guillermo2;|
1Biomimetics and Intelligent Systems Group, University of Oulu, P.O. Box 4500, Oulu, 90014, Finland
2CeDInt-UPM, Universidad Politecnica de Madrid, Campus de Montegancedo sn, Pozuelo de Alarcon, Madrid, 28223, Spain
|Online Access:||PDF Full Text (PDF, 3.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021121060050
|Publish Date:|| 2021-12-10
Sensor devices are becoming omnipresent, supplying data to a wide range of applications. In the building sector, sensors along with other information sources provide the basis for smart building functionalities. Predicting energy loads and inferring occupancy status of spaces are important tasks that promote energy efficiency and user comfort in buildings. For them, as for many other smart building applications, machine learning modelling utilizing sensor data is commonly applied. This article builds understanding of the environment where this kind of machine learning models have to operate by bringing up properties and quality aspects of the public building data provided by indoor sensor devices. This is done by performing a thorough case study on two real life data sets from university campus buildings located in different climates and applying very different sensor network settings. Outcomes include information about heterogeneity, correlations and temporal patterns present in sensor data, and show the need of the building field for better acknowledging the quality deficiencies that sensor data have. Our results aid in assessing and improving the quality of sensor-based indoor data utilized in machine learning modelling, in evaluating whether a data set is representative enough to build a model that is robust under changing conditions in the building, and in choosing an appropriate number of sensors per space when building an indoor wireless sensor network.
Building and environment
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by CHIST-ERA, France and Academy of Finland project ABIDI (grant number CHIST-ERA-17-BDSI-001): Context-aware and Veracious Big Data Analytics for Industrial IoT.
© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).