JSON Dataset of Simulated Building Heat Control for System of Systems Interoperability - Temperature Data Luleå Summer 2018
SND-ID: 2022-45-2. Version: 1. DOI: https://doi.org/10.5878/257p-e437
Download data
Associated documentation
Download all files
Citation
Creator/Principal investigator(s)
Jacob Nilsson - Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering (EISLAB)
Research principal
Luleå University of Technology - Department of Computer Science, Electrical and Space Engineering (EISLAB)
Description
Current approaches to interoperability rely on hand-made adapters or methods using ontological metadata.
This dataset was created to facilitate research on data-driven interoperability solutions.
The data comes from a simulation of a building heating system, and the messages sent within control systems-of-systems. For more information see attached data documentation.
This dataset is used as input for the thermodynamic building simulation found on Github, where it is used to get the outside temperature and corresponding timestamps.
The temperature measurements were downloaded from SMHI.
Data contains personal data
No
Language
Geographic spread
Geographic location: Luleå Municipality
Geographic description: Some temperature data is taken from the SMHI weather station in Luleå
Responsible department/unit
Department of Computer Science, Electrical and Space Engineering (EISLAB)
Research area
Information systems (Standard för svensk indelning av forskningsämnen 2011)
Building technologies (Standard för svensk indelning av forskningsämnen 2011)
Control engineering (Standard för svensk indelning av forskningsämnen 2011)
Communication systems (Standard för svensk indelning av forskningsämnen 2011)
Other electrical engineering, electronic engineering, information engineering (Standard för svensk indelning av forskningsämnen 2011)
Nilsson, J., Delsing, J., & Sandin, F. (2020). Autoencoder Alignment Approach to Run-Time Interoperability for System of Systems Engineering. In IEEE 24th International Conference on Intelligent Engineering Systems (pp. 139–144). https://doi.org/10.1109/INES49302.2020.9147168
URN:
urn:nbn:se:ltu:diva-80561
DOI:
https://doi.org/10.1109/INES49302.2020.9147168
SwePub:
oai:DiVA.org:ltu-80561
Nilsson, J., Delsing, J., Liwicki, M., & Sandin, F. (n.d.). Machine Learning based System–of–Systems Interoperability : A SenML–JSON Case Study. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-87849
URN:
urn:nbn:se:ltu:diva-87849
If you have published anything based on these data, please notify us with a reference to your publication(s). If you are responsible for the catalogue entry, you can update the metadata/data description in DORIS.