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22 May 2023 Research on a new electric power market trading strategy based on power big data
Tao Wang, Junqiang Jia, Jingfeng Xiao, Lulu Liu, Zhongjiao Wang
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Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126400G (2023) https://doi.org/10.1117/12.2673717
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China
Abstract
The power industry has achieved rapid development with the strong support of national policies, which makes the relevant data information show geometric growth. With the further promotion of the power market reform, the market transaction subjects are gradually diversified. In this progress, a good trading strategy is particularly important in order to get more generous profits in the market trading progress. The application of power big data technology can provide security guarantee for electric power production and electric power transaction. Through data mining, data analysis, data extraction and data storage, it can provide a data basis for the development of various transactions in the electric power market. Based on the mining and processing of power big data, this paper builds a two-stage decision-making framework for the power market to study the trading strategies of market participants. The model established in this paper considers the uncertainty in the two-stage decision-making process, which can reflect the risks associated with the decision-making process.
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Tao Wang, Junqiang Jia, Jingfeng Xiao, Lulu Liu, and Zhongjiao Wang "Research on a new electric power market trading strategy based on power big data", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126400G (22 May 2023); https://doi.org/10.1117/12.2673717
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KEYWORDS
Decision making
Data mining
Data processing
Statistical analysis
Industry
Power grids
Power supplies
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