Research Article
Power Grid Investment Demand Forecasting Model Based on Data Mining
@INPROCEEDINGS{10.4108/eai.28-10-2022.2328464, author={Yunhua Cong and Wen Xiang and Ying Wang and Feng Zuo and Yan Zha and Bing Gu}, title={Power Grid Investment Demand Forecasting Model Based on Data Mining}, proceedings={Proceedings of the International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2022, October 28-30, 2022, Shenzhen, China}, publisher={EAI}, proceedings_a={FFIT}, year={2023}, month={4}, keywords={data mining; power grid investment demand forecast; nuclear principal component analysis; support vector machine}, doi={10.4108/eai.28-10-2022.2328464} }- Yunhua Cong
Wen Xiang
Ying Wang
Feng Zuo
Yan Zha
Bing Gu
Year: 2023
Power Grid Investment Demand Forecasting Model Based on Data Mining
FFIT
EAI
DOI: 10.4108/eai.28-10-2022.2328464
Abstract
Since the introduction of the "carbon peak" and "carbon neutral" action plans in 2020, the investment trend needs the joint sustained efforts of the supply side and the demand side. While the supply side adjusts the structure, the demand side also needs to make corresponding responses. Therefore, the future development of power enterprises should focus on the investment strategy and growth path within the key scope of the power grid. This paper identifies the key elements affecting power grid investment based on data mining, and constructs a power grid investment requirement forecasting model stemmed from intelligent mining algorithm, which provides a new method for power grid investment requirement calculating.
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