Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting

Lintao Yang and Honggeng Yang
Additional contact information
Lintao Yang: College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China
Honggeng Yang: College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China

Energies, 2019, vol. 12, issue 8, 1-23

Abstract: Short-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but without a systematic comparative analysis. In this paper, we first compare the most frequently used neural networks’ performance on the load dataset from the State Grid Sichuan Electric Power Company (China). Then, considering the current neural networks’ disadvantages, we propose a new architecture called a gate-recurrent neural network (RNN) based on an RNN for STLF. By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, particularly when the time scale is smaller than 20 min.

Keywords: short-term load forecasting; back-propagation neural network; recurrent neural network; long-short term memory; gate-recurrent neural network (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 (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/8/1433/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/8/1433/ (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:8:p:1433-:d:222669

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 ().