Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Hangzhou, China

Research Article

Investment Demand Forecast for Grid Companies Based on Multiple Linear Regression Analysis

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  • @INPROCEEDINGS{10.4108/eai.19-5-2023.2334303,
        author={Yizheng  Li and Junxian  Ma and Lang  Zhao and Xue  Feng and Dong  Peng and Haiqiong  Yi},
        title={Investment Demand Forecast for Grid Companies Based on Multiple Linear Regression Analysis},
        proceedings={Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19--21, 2023, Hangzhou, China},
        publisher={EAI},
        proceedings_a={ICBBEM},
        year={2023},
        month={7},
        keywords={grid investment; investment demand forecast; investment impact factors; multiple linear regression analysis},
        doi={10.4108/eai.19-5-2023.2334303}
    }
    
  • Yizheng Li
    Junxian Ma
    Lang Zhao
    Xue Feng
    Dong Peng
    Haiqiong Yi
    Year: 2023
    Investment Demand Forecast for Grid Companies Based on Multiple Linear Regression Analysis
    ICBBEM
    EAI
    DOI: 10.4108/eai.19-5-2023.2334303
Yizheng Li1, Junxian Ma2, Lang Zhao1, Xue Feng2,*, Dong Peng1, Haiqiong Yi1
  • 1: State Grid Economic and Technological Research Institute Co.
  • 2: Economic and Technical Research Institute of State Grid Ningxia Electric Power Co.
*Contact email: ysfvab@126.com

Abstract

Grid investment demand refers to the investment made by provincial power companies to ensure that strategic projects and decisions are steadily promoted, while building a safe and reliable grid according to the needs of social and economic development. First, the role of population, GDP, total energy consumption and total social electricity consumption on the investment demand of grid companies is analyzed. Secondly, the correlation between each influencing factor and grid investment demand is analyzed by Pearson correlation coefficient analysis, and then the key factors affecting grid investment demand are selected. Finally, combining the degree of influence of key factors, the future investment demand of grid companies is predicted by multiple regression prediction model.