LAPSE:2023.25759
Published Article
LAPSE:2023.25759
Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network
Shaoqian Pei, Hui Qin, Liqiang Yao, Yongqi Liu, Chao Wang, Jianzhong Zhou
March 29, 2023
Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. However, how to select the optimal feature set and accurately predict multi-step ahead short-term load still faces huge challenges. In this paper, a hybrid feature selection method is proposed, an Improved Long Short-Term Memory network (ILSTM) is applied to predict multi-step ahead load. This method firstly takes the influence of temperature, humidity, dew point, and date type on the load into consideration. Furthermore, the maximum information coefficient is used for the preliminary screening of historical load, and Max-Relevance and Min-Redundancy (mRMR) is employed for further feature selection. Finally, the selected feature set is considered as input of the model to perform multi-step ahead short-term load prediction by the Improved Long Short-Term Memory network. In order to verify the performance of the proposed model, two categories of contrast methods are applied: (1) comparing the model with hybrid feature selection and the model which does not adopt hybrid feature selection; (2) comparing different models including Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) using hybrid feature selection. The result of the experiments, which were developed during four periods in the Hubei Province, China, show that hybrid feature selection can improve the prediction accuracy of the model, and the proposed model can accurately predict the multi-step ahead load.
Keywords
hybrid feature selection, Improved Long Short-Term Memory network, Max-Relevance and Min-Redundancy, multi-step ahead load, short-term load forecasting
Suggested Citation
Pei S, Qin H, Yao L, Liu Y, Wang C, Zhou J. Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network. (2023). LAPSE:2023.25759
Author Affiliations
Pei S: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Qin H: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China [ORCID]
Yao L: Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan 430074, China
Liu Y: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Wang C: Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100044, China
Zhou J: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Journal Name
Energies
Volume
13
Issue
16
Article Number
E4121
Year
2020
Publication Date
2020-08-10
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en13164121, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.25759
This Record
External Link

doi:10.3390/en13164121
Publisher Version
Download
Files
[Download 1v1.pdf] (6.6 MB)
Mar 29, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
61
Version History
[v1] (Original Submission)
Mar 29, 2023
 
Verified by curator on
Mar 29, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.25759
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version