Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

Xin Gao, Xiaobing Li, Bing Zhao, Weijia Ji, Xiao Jing and Yang He
Additional contact information
Xin Gao: School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Xiaobing Li: School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Bing Zhao: School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China
Weijia Ji: School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Xiao Jing: School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Yang He: School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China

Energies, 2019, vol. 12, issue 6, 1-18

Abstract: Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.

Keywords: short-term electricity load forecasting; EMD; correlation analysis; GRU (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/6/1140/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/6/1140/ (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:12:y:2019:i:6:p:1140-:d:216692

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 ().