Creator/Principal investigator(s)
Jacob Nilsson
- 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.
Language
English
Research principal
Responsible department/unit
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering (EISLAB)Data contains personal data
No
Geographic spread
Geographic location: Luleå Municipality
Geographic description: Some temperature data is taken from the SMHI weather station in Luleå
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
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.
Download data
Associated documentation
Description
The data comes in two semicolon-separated (;) csv files, training.csv and test.csv. The train/test split is not random; training data comes from the first 80% of simulated timesteps, and the test data is the last 20%. There is no specific validation dataset, the validation data should instead be randomly selected from the training data. The simulation runs for as many time steps as there are outside temperature values available. The original SMHI data only samples once every hour, which we linea
... Show more..Version 1
https://doi.org/10.5878/1tv7-9x76
Citation
Download citation
Data format / data structure
Other
Creator/Principal investigator(s)
Jacob Nilsson
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering (EISLAB)
License
Download data
Associated documentation
Description
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.
Version 1
https://doi.org/10.5878/257p-e437
Citation
Download citation
Data format / data structure
Numeric
Text
Other
Creator/Principal investigator(s)
Jacob Nilsson
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering (EISLAB)
Copyright
SMHI under Creative Commons Attribution 4.0 SE
License