A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting

Ankit Kumar Srivastava, Ajay Shekhar Pandey, Rajvikram Madurai Elavarasan, Umashankar Subramaniam, Saad Mekhilef and Lucian Mihet-Popa
Additional contact information
Ankit Kumar Srivastava: Department of Electrical Engineering, Dr. Rammanohar Lohia Avdh University, Ayodhya 224001, India
Ajay Shekhar Pandey: Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur 228118, India
Rajvikram Madurai Elavarasan: Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625015, India
Umashankar Subramaniam: Renewable Energy Laboratory, Department of Communications and Networks, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia
Saad Mekhilef: School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Lucian Mihet-Popa: Faculty of Electrical Engineering, Ostfold University College, 1757 Halden, Norway

Energies, 2021, vol. 14, issue 24, 1-16

Abstract: The paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the proposed forecaster is compared with forecasters based on classification tree and regression tree. A hybrid FS method based on the elitist genetic algorithm (GA) and a tree-based method is applied for FS. Making use of selected features, aperformance test of the forecaster was carried out to establish the usefulness of the proposed approach. By way of analyzing and forecasts for day-ahead electricity prices in the Australian electricity markets, the proposed approach is evaluated and it has been established that, with the selected feature, the proposed forecaster consistently outperforms the forecaster with a larger feature set. The proposed method is simulated in MATLAB and WEKA software.

Keywords: price forecasting; feature selection; elitist genetic algorithm; SMO regression; confidence interval (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/24/8455/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/24/8455/ (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:14:y:2021:i:24:p:8455-:d:702668

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 ().