Self-Supervised Adaptive Learning Algorithm for Multi-Horizon Electricity Price Forecasting

Zamee, MA; Lee, Y; Won, D

Self-Supervised Adaptive Learning Algorithm for Multi-Horizon Electricity Price Forecasting

Zamee, MA Lee, Y Won, D

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Access, 2024, 12, pp. 54913-54933
Issue Date:: 2024-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (2.4 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Zamee, MA
dc.contributor.author	Lee, Y
dc.contributor.author	Won, D
dc.date.accessioned	2024-10-16T09:16:54Z
dc.date.available	2024-10-16T09:16:54Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Access, 2024, 12, pp. 54913-54933
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/181427
dc.description.abstract	Forecasting accuracy of electricity prices is crucial to the optimal operation of the electricity market, as improper forecasting can lead to inefficiencies, increased costs, and market instability. Thus, it is highly desired to develop a robust electricity price forecasting framework. The development of an optimal forecasting model depends on the proper choice of exogenous variables, and as the impact/characteristics of the input variables may change over time, thus the choice of appropriate external variables should be a dynamic task. Therefore, it is necessary to develop an online adaptive forecasting model, which will not only continuously forecast but also learn automatically by sensing the changes in the relationship of the variables. To sense the changes and to develop a parsimonious model proper feature engineering is required. Multi-level correlation with multicollinearity has been considered as the feature engineering tool for online training to create an accurate forecasting model. After analyzing existing studies and analyzing the gaps, an approach is proposed, utilizing a General Regression Neural Network (GRNN) with advanced feature engineering and simultaneous adaptive learning, that can outperform traditional models like ANN, RNN, and LSTM in terms of forecasting accuracy.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2024.3389039
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Self-Supervised Adaptive Learning Algorithm for Multi-Horizon Electricity Price Forecasting
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2024-10-16T09:16:52Z
pubs.publication-status	Published
pubs.volume	12

Abstract:

Forecasting accuracy of electricity prices is crucial to the optimal operation of the electricity market, as improper forecasting can lead to inefficiencies, increased costs, and market instability. Thus, it is highly desired to develop a robust electricity price forecasting framework. The development of an optimal forecasting model depends on the proper choice of exogenous variables, and as the impact/characteristics of the input variables may change over time, thus the choice of appropriate external variables should be a dynamic task. Therefore, it is necessary to develop an online adaptive forecasting model, which will not only continuously forecast but also learn automatically by sensing the changes in the relationship of the variables. To sense the changes and to develop a parsimonious model proper feature engineering is required. Multi-level correlation with multicollinearity has been considered as the feature engineering tool for online training to create an accurate forecasting model. After analyzing existing studies and analyzing the gaps, an approach is proposed, utilizing a General Regression Neural Network (GRNN) with advanced feature engineering and simultaneous adaptive learning, that can outperform traditional models like ANN, RNN, and LSTM in terms of forecasting accuracy.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/181427