Short-Term Load Forecasting Based on Pelican Optimization Algorithm and Dropout Long Short-Term Memories–Fully Convolutional Neural Network Optimization

Haonan Wang, Shan Huang, Yue Yin () and Tingyun Gu
Additional contact information
Haonan Wang: School of Electrical Engineering, Sichuan University, Chengdu 610065, China
Shan Huang: School of Electrical Engineering, Sichuan University, Chengdu 610065, China
Yue Yin: School of Electrical Engineering, Sichuan University, Chengdu 610065, China
Tingyun Gu: Electric Power Research Institute of Guizhou Power Grid, Guiyang 550007, China

Energies, 2024, vol. 17, issue 23, 1-15

Abstract: In order to improve the prediction accuracy of short-term power loads in a power system, this paper proposes a short-term load prediction method (POA-DLSTMs-FCN) based on a combination of multi-layer lost long short-term memory (DLSTM) neural networks, fully convolutional neural networks (FCNs) and the pelican optimization algorithm (POA). This method firstly uses DLSTMs to extract the time-series features of the load data, which can effectively capture the dynamic changes in the time series; subsequently, it combines the convolution operation of FCNs to obtain high-resolution information between the load data and the features, which enhances the expressive ability of the model. Through a parallel structure, DLSTMs and FCNs can jointly optimize the information extraction and then construct a more accurate load forecasting model. In addition, the learning rate, the number of hidden neurons and the deactivation probability of the Dropout layer in DLSTMs are optimized by the POA to further enhance the performance of the model. The experimental results show that the proposed optimization method has significant advantages over traditional DLSTMs and FCN-LSTM models in terms of prediction accuracy and stability.

Keywords: short-term load forecasting; pelican optimization algorithm; LSTM; FCN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/23/6115/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/23/6115/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:23:p:6115-:d:1536850

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().