Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model

Zhoufan Chen, Congmin Wang, Longjin Lv, Liangzhong Fan, Shiting Wen () and Zhengtao Xiang ()
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Zhoufan Chen: School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan 442000, China
Congmin Wang: State Grid Zhejiang Electric Power Co., Ltd., Ningbo Power Supply Company, Ningbo 315000, China
Longjin Lv: School of Finance and Information, Ningbo University of Finance and Economics, Ningbo 315000, China
Liangzhong Fan: Shool of Computer and Data Engineering, NingboTech University, Ningbo 315000, China
Shiting Wen: Shool of Computer and Data Engineering, NingboTech University, Ningbo 315000, China
Zhengtao Xiang: School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan 442000, China

Sustainability, 2023, vol. 15, issue 15, 1-16

Abstract: The increasing demand for precise load forecasting for distribution networks has become a crucial requirement due to the continual surge in power consumption. Accurate forecasting of peak loads for distribution networks is paramount to ensure that power grids operate smoothly and to optimize their configuration. Many load forecasting methods do not meet the requirements for accurate data and trend fitting. To address these issues, this paper presents a novel forecasting model called Prophet-LSTM, which combines the strengths of the Prophet model’s high trend fitting and LSTM model’s high prediction accuracy, resulting in improved accuracy and effectiveness of peak load forecasting. The proposed algorithm models the distribution network peak load using the Prophet-LSTM algorithm. The researchers then analyzed the experimental data and model of the algorithm to evaluate its effectiveness. We found that the Prophet-LSTM algorithm outperformed the Prophet and LSTM models individually in peak load prediction. We evaluate the proposed model against commonly used forecasting models using MAE (mean absolute error) and RMSE (root mean square error) as evaluation metrics. The results indicate that the proposed model has better forecasting accuracy and stability. As a result, it can predict the peak load of distribution networks more accurately. In conclusion, this study offers a valuable contribution to load forecasting for distribution networks. The proposed Prophet-LSTM algorithm provides a more precise and stable prediction, making it a promising approach for future applications in distribution network load forecasting.

Keywords: distribution grid; Prophet-LSTM; load forecasting; time series analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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