Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15–17 March 2024, Changsha, China

Research Article

An Early-warning Indicator System of Public Opinion Risk in Widespread Power Outages in City area Based on a Large Regional Power grid in China

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  • @INPROCEEDINGS{10.4108/eai.15-3-2024.2346572,
        author={Yuhui  Guo and Yao  Chen and Zhaohui  Hu and Chuang  Deng and Jinyi  Li},
        title={An Early-warning Indicator System of Public Opinion Risk in Widespread Power Outages in City area Based on a Large Regional Power grid in China},
        proceedings={Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15--17 March 2024, Changsha, China},
        publisher={EAI},
        proceedings_a={PMIS},
        year={2024},
        month={6},
        keywords={public opinion risk; early-warning indicator system;power outages event},
        doi={10.4108/eai.15-3-2024.2346572}
    }
    
  • Yuhui Guo
    Yao Chen
    Zhaohui Hu
    Chuang Deng
    Jinyi Li
    Year: 2024
    An Early-warning Indicator System of Public Opinion Risk in Widespread Power Outages in City area Based on a Large Regional Power grid in China
    PMIS
    EAI
    DOI: 10.4108/eai.15-3-2024.2346572
Yuhui Guo1,*, Yao Chen1, Zhaohui Hu2, Chuang Deng2, Jinyi Li1
  • 1: University of Electronic Science and Technology
  • 2: Sichuan Electric Power Corporation (SGCC SEPC)
*Contact email: gyh@uestc.edu.cn

Abstract

This paper builds an early warning indicator system for public opinion risk based on the practical needs of public opinion management in large-scale urban power outages, and further examines the effectiveness of the early warning indicator system using the case of the "Chengdu high-temperature power restriction" incident. First, semi-structured interviews were conducted with four staff members of the Publicity Department of the State Grid Corporation of China (Sichuan Electricity Branch), who are responsible for public opinion management. Based on the interview data, the four key dimensions of the early warning indicator system were compiled, and the conceptual structure and dimensions of the indicators were refined on the basis of the interview data. Second, the study invited six academic experts on public opinion risk management to analyse the structure and content of the indicator system with Analytic Hierarchy Process, and finally obtained the weights in each indicator system to form a complete early warning indicator system with risk level as the result. Finally, based on the case data and online public opinion data provided by State Grid, the risk level results of the indicator system were used to compare with the risk assessment levels of the six experts and staff.