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---
language:
- en
pretty_name: energy for induction motor simulation
task_categories:
- feature-extraction
- text-classification
- time-series-forecasting
size_categories:
- 10M<n<100M
license: mit
tags:
- code
---
# Dataset Card for energy_induction_motor_simulation
<!-- Provide a quick summary of the dataset. -->
This dataset is simulated for four electrical motors using simulation modeling in MATLAB.
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors.
## Citation [optional]
Le T-T-H, Oktian YE, Jo U, Kim H. Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory. Sensors. 2023; 23(17):7647. https://doi.org/10.3390/s23177647
**BibTeX:**
@Article{s23177647,
AUTHOR = {Le, Thi-Thu-Huong and Oktian, Yustus Eko and Jo, Uk and Kim, Howon},
TITLE = {Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory},
JOURNAL = {Sensors},
VOLUME = {23},
YEAR = {2023},
NUMBER = {17},
ARTICLE-NUMBER = {7647},
URL = {https://www.mdpi.com/1424-8220/23/17/7647},
PubMedID = {37688102},
ISSN = {1424-8220},
DOI = {10.3390/s23177647}
}
@misc {le_2025,
author = { {Le} },
title = { energy_induction_motor_simulation (Revision 9a55484) },
year = 2025,
url = { https://huggingface.co/datasets/Thi-Thu-Huong/energy_induction_motor_simulation },
doi = { 10.57967/hf/4811 },
publisher = { Hugging Face }
}
## Dataset Card Contact
[email protected] |