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Collecting Indoor Environmental Sensor Values for Machine Learning Based Smart Building Control
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.ORCID iD: 0000-0002-1797-1095
Mid Sweden University, Faculty of Science, Technology and Media, Department of Ecotechnology and Suistainable Building Engineering.ORCID iD: 0000-0001-5356-7471
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
2021 (English)In: 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), IEEE, 2021, p. 37-43Conference paper, Published paper (Refereed)
Abstract [en]

This research presents a solution for collecting indoor environmental sensor values and how the gathered sensor values then could be used for green building certification and in turn also machine learning based smart building control. We have created and implemented a proof of concept system consisting of a sensor collecting device using off the shelf hardware to complement the existing sensor information from buildings, as well as a cloud system for persistently storing this data for later usage. We have measured and evaluated our implemented system for our envisioned scenarios. In which we could observe that our proof-of-concept could scale to handle almost four sensor value updates per second at maximum stress, as well as having a latency for uploading a sensor value from our sensor of about 130 ms. Finally, we present our future and ongoing work based on these results which outlines our work for smart building control, green building certification, and the energy signature of buildings. 

Place, publisher, year, edition, pages
IEEE, 2021. p. 37-43
Keywords [en]
cloud, ecotechnology, energy signatures, green classification, industrial internet of things, Internet of Things, machine learning, sensors, smart buildings
National Category
Civil Engineering Computer and Information Sciences
Identifiers
URN: urn:nbn:se:miun:diva-41668DOI: 10.1109/IoTaIS50849.2021.9359717ISI: 000670599800007Scopus ID: 2-s2.0-85102210342ISBN: 9781728194486 (electronic)OAI: oai:DiVA.org:miun-41668DiVA, id: diva2:1537595
Conference
2020 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2020, 27 January 2021 through 28 January 2021
Available from: 2021-03-16 Created: 2021-03-16 Last updated: 2025-09-25Bibliographically approved

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Forsström, StefanDanielski, ItaiZhang, TingtingJennehag, Ulf

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Forsström, StefanDanielski, ItaiZhang, TingtingJennehag, Ulf
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CiteExportLink to record
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Citation style
  • apa
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Output format
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