Forecasting of price signals using deep recurrent models

Venkateswarlu Gundu () and Sishaj P. Simon ()
Additional contact information
Venkateswarlu Gundu: Koneru Lakshmaiah Education Foundation
Sishaj P. Simon: National Institute of Technology

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 11, No 20, 5378-5388

Abstract: Abstract This paper discussed a novel recurrent neural network architecture for electricity price forecasting in a distribution system. Uncertainty in price forecasts misleads utilities in making bidding plans, and investments, and being aware of the risks involved. Accurate price forecasting assists utilities in developing effective bidding strategies and making appropriate investment decisions. Hence, in this paper, a novel feature combination such as the day ahead, similar day, and the combination of both day ahead and similar day using PSO-based LSTM and GRU network models are presented. The proposed method involves the optimal selection of the recurrent network model for electricity price forecasting. Finally, an experimental study is carried out to select the layers and nodes of the network model. In this analysis, the performance of the proposed model is evaluated using the mean absolute percentage error.

Keywords: Mean absolute percentage error; Power Exchange; PSO-based deep network model (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02546-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:ijsaem:v:15:y:2024:i:11:d:10.1007_s13198-024-02546-x

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02546-x

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().