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  2. Experimental Data for the Paper "Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines"
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    Datenpaket: Experimental Data for the Paper "Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines"

    Alternativer Identifier:
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    Verwandter Identifier:
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    Ersteller/in:
    Bielski, Pawel https://orcid.org/0009-0005-3242-9113 [Lehrstuhl IPD Böhm]

    Kottonau, Dustin https://orcid.org/0000-0002-5571-1729 [Institut für Technische Physik]
    Beitragende:
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    Titel:
    Experimental Data for the Paper "Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines"
    Weitere Titel:
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    Beschreibung:
    (Abstract) 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
    (Technical Remarks) # 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).
    Schlagworte:
    Dynamical Systems
    Dynamics Modeling
    Micro Gas Turbine
    Physics-Guided Deep Learning
    Domain Knowledge
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    Herausgeber/in:
    Karlsruhe Institute of Technology
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    Fachgebiet:
    Allgemeines, Hochschulwesen, Wissenschaft und Forschung
    Objekttyp:
    Dataset
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    Status:
    Publiziert
    Eingestellt von:
    kitopen
    Erstellt am:
    Archivierungsdatum:
    2024-04-26
    Archivgröße:
    3,0 MB
    Archiversteller:
    kitopen
    Archiv-Prüfsumme:
    60fb6bc9f0fbb6a4207bf630fabb22dd (MD5)
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