JSON Dataset of Simulated Building Heat Control for System of Systems Interoperability - Temperature Data Luleå Summer 2018
https://doi.org/10.5878/257p-e437
Interoperability in systems-of-systems is a difficult problem due to the abundance of data standards and formats.
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.
Citation and access
Citation and access
Data access level:
Creator/Principal investigator(s):
Research principal:
Data contains personal data:
No
Citation:
Language:
Copyright:
SMHI under Creative Commons Attribution 4.0 SE
Data collection - Non-participant field observation
Data collection - Non-participant field observation
Mode of collection:
Non-participant field observation
Description of the mode of collection:
Temperature data from SMHI
Data collector:
Geographic coverage
Geographic coverage
Geographic location:
Geographic description:
Some temperature data is taken from the SMHI weather station in Luleå
Administrative information
Administrative information
Responsible department/unit:
Department of Computer Science, Electrical and Space Engineering (EISLAB)
Funding
Funding
Funding agency:
- ECSEL Joint Undertaking (JU)
Award number:
826452
Award title:
Arrowhead Tools
Topic and keywords
Topic and keywords
Standard för svensk indelning av forskningsämnen 2025:
Relations
Relations
Related resource:
Is obsoleted by:
Related research data:
Publications
Publications
Citation:
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/INES49302.2020.9147168Opens in a new tab
Citation:
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-87849Opens in a new tab
Metadata
Metadata
Version 1
Luleå University of Technology