Alternativer Identifier:
-
Verwandter Identifier:
-
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:
-
Titel:
Experimental Data for the Paper "Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines"
Weitere Titel:
-
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
Zugehörige Informationen:
-
Sprache:
-
Erstellungsjahr:
Fachgebiet:
Allgemeines, Hochschulwesen, Wissenschaft und Forschung
Objekttyp:
Dataset
Datenquelle:
-
Verwendete Software:
-
Datenverarbeitung:
-
Erscheinungsjahr:
Rechteinhaber/in:
Förderung:
-
Name Speichervolumen Metadaten Upload Aktion

Zugriffe der letzten sechs Monate

Aufrufe der Datenpaket-Seite

110


Downloads des Datenpakets

17


Gesamtstatistik

Zeitraum Aufrufe der Datenpaket-Seite Datenpaket heruntergeladen
Mai 2024 79 17
Apr. 2024 31 0
März 2024 0 0
Feb. 2024 0 0
Jan. 2024 0 0
Dez. 2023 0 0
Vorher 0 0
Gesamt 110 17
Status:
Publiziert
Eingestellt von:
kitopen
Erstellt am:
Archivierungsdatum:
2024-04-26
Archivgröße:
3,0 MB
Archiversteller:
kitopen
Archiv-Prüfsumme:
60fb6bc9f0fbb6a4207bf630fabb22dd (MD5)
Ende des Embargo-Zeitraums:
-