Electricity Spot Market Clearing Price Prediction Model: Algorithm Research and Application Based on Time Series Analysis
Main Article Content
Abstract
Against the backdrop of high-penetration renewable energy integration and increasingly intelligent power communication infrastructures, electricity spot market prices exhibit complex nonlinear fluctuations that directly affect the operational costs and scheduling efficiency of energy-intensive industrial systems. Accurate price forecasting is therefore essential for coordinated energy management and reliable information transmission in modern smart grids. This paper proposes a time-series prediction algorithm integrating fractal analysis and deep learning to capture both long-term memory characteristics and local abrupt variations in electricity price sequences. The method first quantifies long-range correlations using the Hurst exponent derived from rescaled range analysis, then extracts multi-scale fluctuation features through multifractal detrended fluctuation analysis, and finally embeds fractal characteristics into the loss function of a Long Short-Term Memory (LSTM) network to construct a fractal-aware prediction model. A dualwindow strategy is adopted to reconcile the statistical requirements of fractal estimation with temporal responsiveness. Experimental results based on actual clearing data from five domestic spot market pilot regions demonstrate that the proposed model consistently outperforms conventional ARIMA, standard LSTM, VMD-LSTM, CEEMDAN-BERT-LSTM, and Transformer-based methods in both MAPE and RMSE. The proposed framework provides an effective decisionsupport tool for intelligent energy management and offers potential reference value for signal-aware forecasting and information processing in advanced electromagnetic and communication-enabled power systems.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
X. J. Wu and X. D. Li, “Research on short-term electricity price forecasting in power markets based on deep learning,” Mass Standardization, no. 9, pp. 120–122, 2024, doi: 10.3969/j.issn.1007-1350.2024.09.042.
X. L. Guo, D. P. Hua, P. Y. Bao, et al., “Short-term electricity price forecasting method based on improved VMD-PSO-CNN-LSTM,” Journal of Electric Power Science and Technology, vol. 39, no. 2, pp. 35–43, 2024, doi: 10.19781/j.issn.1673-9140.2024.02.005.
Y. Xu, Q. Li, and H. Cui, “Multi-step short-term electricity price forecasting in power markets with high proportion of clean energy and energy storage based on MIC-EEMD-improved Informer,” Power System Technology, vol. 48, no. 3, pp. 949–957, 2024, doi: 10.13335/j.1000-3673.pst.2023.1671.
W. Z. Wu, X. H. Wang, Y. Lu, et al., “Electricity spot market clearing price prediction method based on fractal theory,” Electric Power, vol. 58, no. 9, pp. 183–193, 2025, doi: 10.11930/j.issn.1004-9649.202502005.
Y. F. Fang, D. Liu, J. Chen, et al., “Day-ahead clearing price prediction method driven by net spot bidding space considering medium-and long-term market factors,” Proceedings of the CSEE, vol. 46, no. 2, pp. 506– 519, 2026, doi: 10.13334/j.0258-8013.pcsee.241938.
S. R. Zhang, “Electricity price forecasting under congestion states in simulation systems,” Energy Technology, vol. 22, no. 2, pp. 3–7, 2024.
K. Q. Xu, “Market electricity price forecasting and risk assessment and analysis under high proportion of new energy,” Master’s thesis, North China Electric Power University (Beijing), 2023, doi: 10.27140/d.cnki.ghbbu.2023.001510.
F. Luo, “Research on extreme risk early warning in financial markets based on multifractal theory and unbalanced samples,” Doctoral dissertation, Southwestern University of Finance and Economics, 2023, doi: 10.7666/d.D03053736.
K. L. Lu, P. F. Li, Z. F. He, et al., “Electricity market day-ahead clearing price prediction based on blending ensemble learning model,” Power Demand Side Management, vol. 25, no. 3, pp. 27–32, 2023, doi: 10.3969/j.issn.1009-1831.2023.03.005.
Z. Zhai, Z. Y. Chen, Q. X. Liu, et al., “Power market trading model for distributed resource aggregators considering risk management,” Electric Power, vol. 58, no. 6, pp. 55–56, 2025, doi: 10.11930/j.issn.1004-9649.202411001.