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.
Department of Computer Science, Electrical and Space Engineering (EISLAB)
- Funding agency: ECSEL Joint Undertaking (JU)
- Funding agency's reference number: 826452
- Project name on the application: Arrowhead Tools
Data contains personal data
Geographic location: Luleå Municipality
Geographic description: Some temperature data is taken from the SMHI weather station in Luleå
Information Systems, Building Technologies, Control Engineering, Communication Systems, Other Electrical Engineering, Electronic Engineering, Information Engineering
(The Swedish standard of fields of research 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
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
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.
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..
The simulation data is not meant to be opened and analyzed in spreadsheet software, it is meant for training machine learning models.
It is recommended to open the data with the pandas library for Python, available at https://pypi.org/project/pandas/. Show less..
Data format / data structure
- Mode of collection: Simulation
- Description of the mode of collection: Building temperature simulation.
- Data collector: Luleå University of Technology
- Instrument: Python script
- Source of the data: Events/Interactions, Physical objects
DescriptionThis 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 format / data structure
- Mode of collection: Non-participant field observation
- Description of the mode of collection: Temperature data from SMHI
- Data collector: SMHI
SMHI under Creative Commons Attribution 4.0 SE