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Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part III

Research Article

Forecasting Method of Power Consumption Information for Power Users Based on Cloud Computing

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-50577-5_22,
        author={Chen Dai and Yukun Xu and Chao Jiang and Jingrui Yan and Xiaowei Dong},
        title={Forecasting Method of Power Consumption Information for Power Users Based on Cloud Computing},
        proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part III},
        proceedings_a={ICMTEL PART 3},
        year={2024},
        month={2},
        keywords={Cloud Computing Power Consumption Information of Power Users Multiple Regression Analysis Prediction Method},
        doi={10.1007/978-3-031-50577-5_22}
    }
    
  • Chen Dai
    Yukun Xu
    Chao Jiang
    Jingrui Yan
    Xiaowei Dong
    Year: 2024
    Forecasting Method of Power Consumption Information for Power Users Based on Cloud Computing
    ICMTEL PART 3
    Springer
    DOI: 10.1007/978-3-031-50577-5_22
Chen Dai1,*, Yukun Xu1, Chao Jiang1, Jingrui Yan2, Xiaowei Dong2
  • 1: State Grid Shanghai Municipal Electric Power Company
  • 2: LongShine Technology Group Co., Ltd.
*Contact email: a2555421451@126.com

Abstract

In order to realize the real-time balance of power demand and effectively avoid the waste of power, it is necessary to forecast the power consumption. Under this background, a forecasting method of power consumption information for power users based on cloud computing is designed. The prediction model framework is designed based on cloud computing technology. Carry out abnormal data processing, missing data filling and normalization for power consumption data. Calculate the correlation degree and select the influencing factors of power consumption of power users. Combined with multiple regression analysis, the forecasting model of electricity consumption information of power users is constructed. The results show that the mean absolute percentage error (MAPS), the root mean square error (RMSE) and the equalization coefficient (EC) of the method are the minimum and the maximum, which proves the accuracy of the method.

Keywords
Cloud Computing Power Consumption Information of Power Users Multiple Regression Analysis Prediction Method
Published
2024-02-21
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-50577-5_22
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