JSON Dataset of Simulated Building Heat Control for System of Systems Interoperability
SND-ID: 2022-45-1. Version: 1. DOI: https://doi.org/10.5878/1tv7-9x76
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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
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.
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 linearly interpolate to get one temperature sample every ten seconds. The data saved at each time step consists of 34 JSON message
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.
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 linearly interpolate to get one temperature sample every ten seconds. The data saved at each time step consists of 34 JSON messages (four per room and two temperature readings from the outside), 9 temperature values (one per room and outside), 8 setpoint values, and 8 actuator outputs. The data associated with each of those 34 JSON-messages is stored as a single row in the tables. This means that much data is duplicated, a choice made to make it easier to use the data.
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 contains personal data
No
Language
Data format / data structure
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
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