Simplicity in dynamic and competitive electricity markets: A case study on enhanced linear models versus complex deep-learning models for day-ahead electricity price forecasting

Xuehui Mao, Shanlin Chen, Hanxin Yu, Liwu Duan, Yingjie He and Yinghao Chu

Applied Energy, 2025, vol. 383, issue C, No S0306261924025856

Abstract: In the transitioning electricity market of China, accurate forecasting of Day-Ahead Electricity Prices (DAEP) is crucial for strategic planning and profit optimization of market participants. It plays a significant role in resource allocation and in enhancing the efficiency of the energy system. DAEP forecasting in complex electricity markets is challenging due to a multitude of factors, including end-user consumption patterns and physical elements like network losses and transmission congestion. Furthermore, DAEP bidding strategies are often entwined with strategic gaming behavior. Motivated by this, we introduce a novel enhanced linear framework designed to optimize the trade-off between preserving historical patterns (the memory function) and extending predictions to new situations (the generalization function) in DAEP forecasting. The framework employs a linear network to capture data trends and Multi-Layer Perceptron networks for the robust extraction of intricate features and generalization. The proposed enhanced linear framework is developed and evaluated using real-world data from 3 geographically distinct power plants in Guangdong, the province with the highest economic scale and electricity consumption in China. Our approach outperforms representative deep-learning methods, including the Long Short-Term Memory model and Transformer models, with improvements of RMSE up to 26.64% and 51.80%, respectively. Additionally, the results reveal that complex models do not always outperform more straightforward ones in real-world markets characterized by extensive interaction and competition. This indicates the proposed framework provides a straightforward but effective method for time-series DAEP forecasting within the competitive electricity markets. Accurate DAEP forecasting can enhance grid security, facilitate optimal resource allocation, and promote the integration of green and low-carbon power sources into the urban energy system.

Keywords: Electricity price; Electricity trading; Day-ahead forecasting; Time-series forecasting; Machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924025856
Full text for ScienceDirect subscribers only

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:eee:appene:v:383:y:2025:i:c:s0306261924025856

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.125201

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().