Research on time series analysis of data products in electric power marketing management information platform based on recurrent neural networks

Leilei Wang, Pingfei Zhu, Junyan Liu, Yukun Wang, Ling Bai and Xinpo Zhu

International Journal of Low-Carbon Technologies, 2025, vol. 20, 659-66

Abstract: The power marketing management information platform can promote the energy industry's decision-making ability for power marketing business and improve demand-side management. Due to the large number of power marketing users and heterogeneous data, traditional time series analysis methods are difficult to flexibly deal with this scenario, and deep learning methods based on feedforward neural networks have weak modeling capabilities for the time dimension of data, making it difficult to effectively use these data to support decision-making. To this end, this study proposes a new time series analysis model for the power marketing management information platform, which uses a recurrent neural network (RNN) enhanced by an attention mechanism. By integrating the long-term time dependency modeling capability of RNN and the adaptive extraction capability of the attention mechanism, the model can efficiently represent time series data. Experimental evaluation shows that our method greatly outperforms traditional models and achieves higher prediction accuracy on several key performance indicators.

Keywords: machine learning; power marketing; recurrent neural network; self-attention mechanism; time series analysis (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:659-66.

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