Proceedings of the International Conference on Industrial Design and Environmental Engineering, IDEE 2023, November 24–26, 2023, Zhengzhou, China

Research Article

Early Warning Method of Abnormal Energy Consumption in Public Buildings Based on Multi-Level Analysis and Adaptive Weight

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  • @INPROCEEDINGS{10.4108/eai.24-11-2023.2343367,
        author={Jiani  Zeng and Longfei  Ma and Baoqun  Zhang and Xin  Gao and Caie  Hu and Siyue  Lu and Hui  Xu},
        title={Early Warning Method of Abnormal Energy  Consumption in Public Buildings Based on Multi-Level  Analysis and Adaptive Weight },
        proceedings={Proceedings of the International Conference on Industrial Design and Environmental Engineering, IDEE 2023, November 24--26, 2023, Zhengzhou, China},
        publisher={EAI},
        proceedings_a={IDEE},
        year={2024},
        month={2},
        keywords={multi-level analysis; adaptive weight; public buildings; abnormal energy  consumption; early warning method},
        doi={10.4108/eai.24-11-2023.2343367}
    }
    
  • Jiani Zeng
    Longfei Ma
    Baoqun Zhang
    Xin Gao
    Caie Hu
    Siyue Lu
    Hui Xu
    Year: 2024
    Early Warning Method of Abnormal Energy Consumption in Public Buildings Based on Multi-Level Analysis and Adaptive Weight
    IDEE
    EAI
    DOI: 10.4108/eai.24-11-2023.2343367
Jiani Zeng1,*, Longfei Ma1, Baoqun Zhang1, Xin Gao1, Caie Hu1, Siyue Lu1, Hui Xu1
  • 1: State Grid Beijing Electric Power Company
*Contact email: 779386503@qq.com

Abstract

In order to make the energy consumption of public buildings more reasonable, it is necessary to warn the changes of energy consumption. Therefore, an early warning method of abnormal energy consumption of public buildings based on multi-level analysis and adaptive weight is proposed. By analyzing the data variation between different levels, we can find out the existing abnormal data, optimize the abnormal data target and analyze the weight, so as to get the preprocessing results. Calculate the total energy consumption of public buildings in the life cycle, including the abnormal energy consumption of public buildings and the operating energy consumption; Input samples, use the minimum proportional distance to classify, output the weight results, get the output network early warning value, and get the final early warning result of abnormal energy consumption of public buildings. The experimental results show that this method can provide more stable and reliable early warning results, and the results are close to the actual energy consumption results.