Similar-Scenario Retrieval and Intelligent Hyperparameter Optimization for Day-Ahead Electricity Price Forecasting
DOI:
https://doi.org/10.4108/ew.14570Keywords:
Day-ahead market-clearing price, similar-scenario retrieval, dung beetle optimization, CNN, BiLSTM, attention mechanismAbstract
INTRODUCTION: Day-ahead electricity prices exhibit pronounced volatility, nonlinearity, and temporal dependence. Training models on all available historical observations may introduce irrelevant information and reduce forecasting reliability.
OBJECTIVES: This study proposes a hybrid forecasting framework that integrates same-period similar-scenario retrieval with intelligent hyperparameter optimization to improve the forecasting performance of day-ahead market-clearing prices. By refining the composition of the training dataset and optimizing the network configuration, the proposed framework enhances the model’s ability to learn the underlying dynamics of historical electricity prices.
METHODS: First, similarity measures are used to evaluate historical electricity price sequences from corresponding periods. Historical observations that are highly relevant to the target forecasting period are then selected to construct a tailored training dataset. Subsequently, a hybrid CNN–BiLSTM–AM forecasting model is developed. Within this architecture, the convolutional neural network (CNN) extracts local patterns and salient features from the input data, while the bidirectional long short-term memory network (BiLSTM) captures long-term temporal dependencies in both forward and backward directions. The attention mechanism (AM) adaptively assigns weights to the learned representations, thereby emphasizing features that contribute more substantially to the final prediction. The dung beetle optimization (DBO) algorithm is further employed to optimize the key hyperparameters of the model. Finally, the effectiveness of the proposed method is evaluated using day-ahead market-clearing price data from the Miaojiaba Hydropower Station in the Bailong River Basin, Gansu Province, China.
RESULTS: The empirical results demonstrate that the similarity-scenario-based sample reconstruction strategy effectively enhances the model’s ability to capture historical electricity price patterns. Taking a representative market price of 100 CNY/MWh as the reference, the proposed method reduces the standard deviation of forecasting errors by approximately 26 CNY/MWh compared with a conventional BiLSTM model without historical similar-sequence selection, thereby helping to mitigate potential revenue losses caused by electricity price forecasting deviations.
CONCLUSION: The proposed framework effectively combines targeted sample selection, spatiotemporal feature learning, attention-based weighting, and hyperparameter optimization. It provides a reliable tool for day-ahead bidding decisions and supports efficient electricity resource allocation.
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References
[1] Feng S, Zhang X. An electricity-carbon joint market optimization model for coal-fired power system under China's "dual-carbon" target[J]. Journal of Cleaner Production, 2023; 423: 138746.
[2] Zhao X, Hu S, Wang H, et al. Energy, economic, and environmental impacts of electricity market-oriented reform and the carbon emissions trading: A recursive dynamic CGE model in China[J]. Energy, 2024; 298: 131416.
[3] Iftikhar H, Turpo-Chaparro J E, Rodrigues P C, et al. Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition-Combination Technique[J]. Energies, 2023; 16(18): 6669.
[4] Bottieau J, Wang Y, De Greve Z, et al. Interpretable Transformer Model for Capturing Regime Switching Effects of Real-Time Electricity Prices[J]. IEEE Transactions on Power Systems, 2023; 38(3): 2162-2176.
[5] Abroun M, Jahangiri A, Shamim A G, et al. Predicting long-term electricity prices using modified support vector regression method[J]. Electrical Engineering, 2024; 106: 4103-4114.
[6] Tang C, Ye Z, Zhao H, et al. DeepSCNN: a simplicial convolutional neural network for deep learning[J]. Applied Intelligence, 2025; 55: 281.
[7] Xu Y, Huang X, Gao Z, et al. A novel electricity price forecasting approach based on multi-attention feature fusion model optimized by variational mode decomposition[J]. Measurement, 2025; 253: 117596.
[8] Dong J, Dou X, Bao A, et al. Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China[J]. Sustainability, 2022; 14(13): 7767.
[9] Kosana V, Madasthu S, Teeparthi K. A novel hybrid framework for wind speed forecasting using autoencoder-based convolutional long short-term memory network[J]. International Transactions on Electrical Energy Systems, 2021; 31(11): e13072.
[10] Vlah D, Sepetanc K, Pandzic H. Solving Bilevel Optimal Bidding Problems Using Deep Convolutional Neural Networks[J]. IEEE Systems Journal, 2023; 17(2): 2767-2778.
[11] Liu W, Mao Z. Short-term photovoltaic power forecasting with feature extraction and attention mechanisms[J]. Renewable Energy, 2024; 226: 120437.
[12] Salgotra R, Sharma P, Raju S, et al. A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference[J]. Archives of Computational Methods in Engineering, 2024; 31: 1749-1822.
[13] Ghimire S, Deo R C, Casillas-Perez D, et al. Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach[J]. Applied Energy, 2024; 374: 123920.
[14] Nie Y, Li P, Wang J, et al. A novel multivariate electrical price bi-forecasting system based on deep learning , a multi-input multi-output structure and an operator combination mechanism[J]. Applied Energy, 2024; 366: 123233.
[15] Wang K, Yu M, Niu D, et al. Short-term electricity price forecasting based on similarity day screening, two-layer decomposition technique and Bi-LSTM neural network[J]. Applied Soft Computing, 2023; 136: 110018.
[16] Zhang X. Short-term electrical load forecasting strategy based on EEMD-SSA-BiLSTM[J]. Signal Image and Video Processing, 2025; 19: 751.
[17] Xue J, Shen B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2023; 79: 7305-7336.
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Copyright (c) 2026 Qingzhong Wang, Xi Fu, Weiguo Xiao, Chuankai He, Xiaogang Xie, Jidong Li
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