<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Amidst the swift advancement of smart grid technology, traditional power dispatching methods have become inadequate in addressing escalating power needs and intricate system management prerequisites. By incorporating a deep learning model, we have refined these methods, facilitating data-driven dispatching decisions and optimizing power resource allocation and dispatching efficiency. Our experimental outcomes reveal that the Long Short-Term Memory network (LSTM) excels in handling intricate time series data, boasting superior accuracy and convergence rates compared to the Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Detailed performance evaluations confirm LSTM’s proficiency in capturing long-term dependencies and processing time series traits inherent in power dispatching data. Furthermore, a 10-fold cross-validation underscores the LSTM model’s stability and generalizability. In essence, this study concludes that in the realm of power dispatching automation, the LSTM deep learning model demonstrates remarkable effectiveness and holds vast potential, anticipating crucial support for the reliable operation and optimal dispatching of the electrical power system (EPS).</p>
</abstract>

Amidst the swift advancement of smart grid technology, traditional power dispatching methods have become inadequate in addressing escalating power needs and intricate system management prerequisites. By incorporating a deep learning model, we have refined these methods, facilitating data-driven dispatching decisions and optimizing power resource allocation and dispatching efficiency. Our experimental outcomes reveal that the Long Short-Term Memory network (LSTM) excels in handling intricate time series data, boasting superior accuracy and convergence rates compared to the Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Detailed performance evaluations confirm LSTM’s proficiency in capturing long-term dependencies and processing time series traits inherent in power dispatching data. Furthermore, a 10-fold cross-validation underscores the LSTM model’s stability and generalizability. In essence, this study concludes that in the realm of power dispatching automation, the LSTM deep learning model demonstrates remarkable effectiveness and holds vast potential, anticipating crucial support for the reliable operation and optimal dispatching of the electrical power system (EPS).

Application and Performance Analysis of Deep Learning Models in Power Dispatching Automation

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Amidst the swift advancement of smart grid technology, traditional power dispatching methods have become inadequate in addressing escalating power needs and...

Application and Performance Analysis of Deep Learning Models in Power Dispatching Automation

Data publikacji: 13 cze 2024

Zakres stron: -

Otrzymano: 25 lut 2024

Przyjęty: 09 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1484

Słowa kluczowe
Power dispatching, Automation, Deep learning, Stability

© 2024 Liu Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application and Performance Analysis of Deep Learning Models in Power Dispatching Automation

Data publikacji: 13 cze 2024

Zakres stron: -

Otrzymano: 25 lut 2024

Przyjęty: 09 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1484

Słowa kluczowePower dispatching, Automation, Deep learning, Stability

© 2024 Liu Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Power dispatching, Automation, Deep learning, Stability