Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives

Chai, Shanglei; Li, Qiang; Abedin, Mohammad; Lucey, Brian M.

doi:10.1016/j.ribaf.2023.102132

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Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives

Shanglei Chai, Qiang Li, Mohammad Abedin

, Brian M. Lucey

Research in International Business and Finance, Volume: 67, Start page: 102132

Swansea University Author: Mohammad Abedin

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Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy.
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DOI (Published version): 10.1016/j.ribaf.2023.102132

Abstract

Accurate electricity price forecasting (EPF) is crucial to participants and decision-makers within the electricity market. This paper reviews 62 screened literature works on EPF during 2012–2022 in terms of model structure and determinants of electricity price and discusses the evaluation process, m...

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Published in:	Research in International Business and Finance
ISSN:	0275-5319 1878-3384
Published:	Elsevier BV 2024
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa64760

first_indexed	2023-10-17T20:16:08Z
last_indexed	2024-11-25T14:14:41Z
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spelling	2023-11-23T16:15:34.0133863 v2 64760 2023-10-17 Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-10-17 CBAE Accurate electricity price forecasting (EPF) is crucial to participants and decision-makers within the electricity market. This paper reviews 62 screened literature works on EPF during 2012–2022 in terms of model structure and determinants of electricity price and discusses the evaluation process, model type, research sample, and prediction horizon. From the above efforts, we find that (1) data preprocessing and model optimization are often used to improve forecasting model accuracy; while performance evaluation is essential, extensive performance evaluation benchmarking is still missing; (2) considering electricity price determinants can significantly improve forecasting model accuracy, but there is disagreement over how many and which determinants should be accounted for; (3) while most existing research focuses on point forecasting, interval and density forecasting are more responsive to the range and uncertainty of electricity price changes. Journal Article Research in International Business and Finance 67 102132 Elsevier BV 0275-5319 1878-3384 Determinants of electricity price, Dual decomposition method, Electricity price forecasting, Model optimization, Model structure 31 1 2024 2024-01-31 10.1016/j.ribaf.2023.102132 http://dx.doi.org/10.1016/j.ribaf.2023.102132 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University This work was supported by the National Social Science Foundation of China (No. 20BJL058). 2023-11-23T16:15:34.0133863 2023-10-17T21:14:59.5086318 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Shanglei Chai 1 Qiang Li 2 Mohammad Abedin 0000-0002-4688-0619 3 Brian M. Lucey 4 64760__29097__9ffa29c9bc6b4624aec4541042af8b24.pdf 64760.AAM.pdf 2023-11-23T16:13:22.9973848 Output 2184609 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy. true eng https://creativecommons.org/licenses/by/4.0/
title	Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives
spellingShingle	Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives Mohammad Abedin
title_short	Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives
title_full	Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives
title_fullStr	Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives
title_full_unstemmed	Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives
title_sort	Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives
author_id_str_mv	4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv	4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author	Mohammad Abedin
author2	Shanglei Chai Qiang Li Mohammad Abedin Brian M. Lucey
format	Journal article
container_title	Research in International Business and Finance
container_volume	67
container_start_page	102132
publishDate	2024
institution	Swansea University
issn	0275-5319 1878-3384
doi_str_mv	10.1016/j.ribaf.2023.102132
publisher	Elsevier BV
college_str	Faculty of Humanities and Social Sciences
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hierarchy_top_title	Faculty of Humanities and Social Sciences
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department_str	School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url	http://dx.doi.org/10.1016/j.ribaf.2023.102132
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description	Accurate electricity price forecasting (EPF) is crucial to participants and decision-makers within the electricity market. This paper reviews 62 screened literature works on EPF during 2012–2022 in terms of model structure and determinants of electricity price and discusses the evaluation process, model type, research sample, and prediction horizon. From the above efforts, we find that (1) data preprocessing and model optimization are often used to improve forecasting model accuracy; while performance evaluation is essential, extensive performance evaluation benchmarking is still missing; (2) considering electricity price determinants can significantly improve forecasting model accuracy, but there is disagreement over how many and which determinants should be accounted for; (3) while most existing research focuses on point forecasting, interval and density forecasting are more responsive to the range and uncertainty of electricity price changes.
published_date	2024-01-31T02:43:21Z
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Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives

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