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Distribution Line Load Predicting and Heavy Overload Warning Model Based on Prophet Method

Author

Listed:
  • Longjin Lv

    (School of Finance and Information, Ningbo University of Finance and Economics, Ningbo 315000, China)

  • Lihua Luo

    (State Grid Zhejiang Cixi Power Supply Co., Ltd., Ningbo 315100, China)

  • Yueping Yang

    (State Grid Zhejiang Electric Power Co., Ltd., Ningbo Power Supply Company, Ningbo 315000, China)

Abstract

The load prediction of distribution network lines and the accurate prediction of impending overload lines can provide an important reference for the formulation of the power supply plan of distribution networks. This paper designs a line load predicting and heavy overload early warning model based on the Prophet method, where the time series decomposition and machine learning technologies are used. Firstly, we used the 5-day moving average to automatically fill the missing values in the data and automatically detect and correct the abnormal values in the data. Then, we decomposed the prediction model into the trend component, periodic component, and data mutation component by fully considering the periodicity, seasonality, holidays, and other factors of power data, which effectively improves the prediction accuracy and gives early warning of potential heavy overload risk. Finally, we tested the model according to the processing speed, root-mean-squared error (RMSE), recognition accuracy, and overload warning hit rate. The results showed that the model obtained in this paper has high accuracy and practicability.

Suggested Citation

  • Longjin Lv & Lihua Luo & Yueping Yang, 2022. "Distribution Line Load Predicting and Heavy Overload Warning Model Based on Prophet Method," Sustainability, MDPI, vol. 14(21), pages 1-10, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13697-:d:950292
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    References listed on IDEAS

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    2. Sadaei, Hossein Javedani & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha & Lee, Muhammad Hisyam, 2019. "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, Elsevier, vol. 175(C), pages 365-377.
    3. Ashish Shrestha & Bishal Ghimire & Francisco Gonzalez-Longatt, 2021. "A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System," Energies, MDPI, vol. 14(11), pages 1-15, June.
    4. Yu Jin & Honggang Guo & Jianzhou Wang & Aiyi Song, 2020. "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-32, November.
    5. Yin, Linfei & Xie, Jiaxing, 2021. "Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems," Applied Energy, Elsevier, vol. 283(C).
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    Cited by:

    1. Zhoufan Chen & Congmin Wang & Longjin Lv & Liangzhong Fan & Shiting Wen & Zhengtao Xiang, 2023. "Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model," Sustainability, MDPI, vol. 15(15), pages 1-16, July.

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