Research on BiLSTM-Attention short-term electricity price forecasting considering similar days
Authors: 
Kexin Wang, Hanxu Cao, Junyu Wang, Tongtao Ma, Bofeng Xu, Zongyao Wang, Ke Luo, Yaozhen HuangAuthors Info & Claims
Kexin Wang, Hanxu Cao, Junyu Wang, Tongtao Ma, Bofeng Xu, Zongyao Wang, Ke Luo, Yaozhen HuangAuthors Info & ClaimsPages 362 - 367
Abstract
Short-term electricity price forecasting has always been one of the important issues in the electricity market. An accurate and applicable electricity price forecasting method can effectively avoid the risk of the trading market to maximize the economic effect. With the development of deep neural networks, machine learning methods have been widely used in the research direction of electricity price forecasting. This paper proposes a hybrid model of short-term electricity price forecasting based on BiLSTM-Attention. The model first uses the similar day method to select the similar days of the day to be predicted, and then introduces the BiLSTM model to predict the short-term electricity price. The results show that the proposed model can obtain high-precision short-term electricity price forecasting results in the ideal electricity market.
References
[1]
Gao L, Peng Q, Yu J, etc. Research on data mining algorithm based on customer electricity consumption behavior [J]. Power system equipment, 2020 (1): 130-131.
[2]
Kang Z, Kang Z. Electricity market short-term electricity price forecasting based on big data analysis technology [J]. Automation technology and application, 2023,42 (07): 95-98. (2023) 07-0095-04.
[3]
Liu Z, Fu X, Gao F, etc. Forecasting method of clearing price in Zhejiang power market based on neural network [J]. Power system automation, 2002 (09): 49-52.
[4]
LU J, ZHANG Q, YANG Z, Short-term load forecasting method based on CNN-LSTM hybrid neural network model[J]. Automation of Electric Power Systems, 2019, 43(8):131-137.
[5]
Wang Y.Research on power load forecasting method based on CNN-BiLSTM algorithm [D]. Xi 'an : Xi 'an University of Technology, 2021.
[6]
Ge S, Jia O, Liu H. Short-term power load forecasting based on genetic grey neural network model under the condition of real-time electricity price [J]. Grid technology, 2012, 36 (01): 224-229. Ps t.2012.01.041
[7]
Dong F, Zhang S, Tan Z, etc. Application of wavelet neural network based on genetic algorithm in electricity price forecasting [J]. Computer Engineering, 2005 (04):32-33+55.
[8]
Xie Q, Dong L, She X. Short-term electricity price forecasting based on Attention-GRU [J]. Power system protection and control, 2020, 48 (23): 154-160.
[9]
Shen S, Li Q. CNN-LSTM short-term stock price prediction based on attention mechanism [J]. Software, 2022, 43 (2): 73-75.
[10]
Yang C, Ran Q, Luo D, etc. CNN-BIGRU short-term electricity price forecasting based on attention mechanism [J/OL]. Journal of Power System and Its Automation: 1-10, 2023.09.13.
[11]
Guan P, Li B, Lv X, Attention-enhanced bidirectional LSTM sentiment analysis [J]. Chinese Journal of Information, 2019, 33 (2): 105-111.
[12]
Han S, Hu F, Chen Z Day ahead market marginal price forecasting based on GCN-LSTM [J]. Proceedings of the CSEE, 2022,42 (9): 3276-3284.
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Published In
November 2023
1156 pages
ISBN:9798400716478
DOI:10.1145/3656766
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 01 June 2024
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ICBAR 2023
ICBAR 2023: 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management
November 24 - 26, 2023
Chengdu, China
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