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

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

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

Swansea University Author: Abedin Abedin

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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
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URI: https://cronfa.swan.ac.uk/Record/cronfa64760
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spelling v2 64760 2023-10-17 Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-10-17 BAF 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 Accounting and Finance COLLEGE CODE BAF 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 Abedin Abedin 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
Abedin 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_***_Abedin Abedin
author Abedin Abedin
author2 Shanglei Chai
Qiang Li
Abedin 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
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
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
document_store_str 1
active_str 0
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 2024-01-31T16:15:35Z
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score 11.012678

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