2026-10-12T16:51:14Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/sLJiahifxvfDKMEc2026-10-09T10:54:07Zradar4kit

https://dx.doi.org/10.35097/sLJiahifxvfDKMEc Bielski, Pawel Kottonau, Dustin Experimental Data for the Paper "Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines" Karlsruhe Institute of Technology 2024 Other Allgemeines, Hochschulwesen, Wissenschaft und Forschung dataset Dataset info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode These are experimental data for the paper: Pawel Bielski, Aleksandr Eismont, Jakob Bach, Florian Leiser, Dustin Kottonau, and Klemens Böhm. 2024. Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines, 15th ACM International Conference on Future Energy Systems (e-Energy '24), Singapore The data consist of: 1. experimental time series data collected from a micro gas turbine 2. results from the experiments and the corresponding code to create plots used in the paper The corresponding GitHub repository: https://github.com/Energy-Theory-Guided-Data-Science/Gas-Turbine # Micro Gas Turbine Data ## Overview These experimental data support the paper *"Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines", presented at 15th ACM International Conference on Future Energy Systems (e-Energy '24), Singapore.* The data was collected from a commercial micro gas turbine designed for residential use, generating approximately 3 kW of electrical power. Its purpose was to model the turbine's behavior over time using machine learning techniques. ## Folder Structure - `data`: Contains 8 experimental time series data in CSV format, collected from the micro gas turbine. - `plots`: Includes results from experiments and the code used to generate plots from the paper. - `plots/create_plots.ipynb`: A Jupyter notebook containing code to create the plots. ## Time Series Data Each time series represents a separate experiment where the input control voltage was varied over time, and the resulting output electrical power of the micro gas turbine was measured. The data has a resolution of approximately 1 second and is structured in a CSV file with the following columns: - `time`: Time in seconds, denoted as $t$. - `input_voltage`: Input control voltage in volts, representing the control signal $x_t$. - `el_power`: electrical power in watts, representing the output signal $y_t$. ## Prediction Task The data was used for a time-series prediction task, aiming to predict `el_power` based on `input_voltage`. In the paper, the objective was to forecast the output $y_t$ given the control inputs $x_t, x_{t-1}, \dots, x_{t-N+1}$. ### Additional Information Requirements for running `create_plots.ipynb`: - Python 3.8.17 - Jupyter Notebook 6.5.4 - Pandas 1.2.2 - Matplotlib 3.5.2 - Seaborn 0.12.2 **When using this dataset, please cite the following paper:** *Pawel Bielski, Aleksandr Eismont, Jakob Bach, Florian Leiser, Dustin Kottonau, and Klemens Böhm. 2024. Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines, 15th ACM International Conference on Future Energy Systems (e-Energy '24), Singapore.* For more details and the code used in the experiments, visit the GitHub [repository](https://github.com/Energy-Theory-Guided-Data-Science/Gas-Turbine). Dynamical Systems Dynamics Modeling Micro Gas Turbine Physics-Guided Deep Learning Domain Knowledge application/x-tar