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 and Jianzhong Zhou
Additional contact information
Shaoqian Pei: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Hui Qin: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Liqiang Yao: Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan 430074, China
Yongqi Liu: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Chao Wang: Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100044, China
Jianzhong Zhou: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2020, vol. 13, issue 16, 1-23

Abstract: 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: short-term load forecasting; Max-Relevance and Min-Redundancy; Improved Long Short-Term Memory network; multi-step ahead load; hybrid feature selection (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/16/4121/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/16/4121/ (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:13:y:2020:i:16:p:4121-:d:396734

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 ().