Author(s)

Qinzhi Yu ¹

Affiliation(s)

¹ Beijing Technology and Business University, Beijing, China

Corresponding Author

Qinzhi Yu

ABSTRACT

Driven by the strategic goal of "Double Carbon", the power industry, as an important field of energy structure transformation and carbon emission reduction, is facing multiple challenges such as balance between supply and demand, optimal allocation of resources and carbon emission control. As the core path of modern industrial development, the integration of informatization and industrialization provides strong support for power forecasting and effectively. Power forecasting improves the intelligent and refined management level of power system. Accurate supply-demand technology plays an important role in the power industry through matching, resource scheduling and carbon. In practical applications, power prediction and the integration of informatization and industrialization still face economic challenges, such as high costs of technological investment, insufficient data sharing and standardization, high costs of technical operation and maintenance as well as long investment return cycle. Against the backdrop of the "Dual Carbon" goals, by combining with the technical advantages of the informatization and industrialization, this paper explores the role of power prediction in energy management, resource allocation and carbon emission reduction, analyzes its crucial position and development prospects in the power industry. With the aim of providing a reference for the power industry to achieve sustainable and high-quality development, this paper also proposes paths and strategies to address the current challenges.

KEYWORDS

Double Carbon target; Power forecasting; Industrialization; Informatization, economic impact

CITE THIS PAPER

Qinzhi Yu, Research on Power Forecasting and Its Economic Impact Based on Integration of Industrialization and Informatization under the Background of Double Carbon. Financial Engineering and Risk Management (2025) Vol. 8: 91-94. DOI: http://dx.doi.org/10.23977/ferm.2025.080112.

REFERENCES

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[2] Tang Zhu, Xiao Yuhang, Guo Chun, et al. Short-term Power Load Forecasting Based on CEEMDAN Modal Decomposition and TCN-BiGRU [J]. Smart Power, 2024, 52(12): 59-64+72.
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[4] Guo Yuan, Zhang Xuecheng, Dong Zhenbiao, et al. Short-term Power Load Forecasting of Residential Buildings Based on TCN-BiLSTM-AM [J]. Modern Electronics Technique, 2024, 47(19): 100-108.
[5] Liu Tan, Liu Na, Liu Guiping, et al. Overview of the Application and Research Directions of Deep Learning Methods in Wind Power Prediction [J/OL]. Journal of Frontiers of Computer Science and Technology, 1-24 [2024-12-25]. 

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