Line Loss Rate Prediction Method Based on Deep Learning with Long Short Term Memory

Hao, Jia; Deng, Yaping; Qiu, Xiaodong; Tong, Xiangqian; Li, Pengcheng; Li, Feng

doi:10.1109/iciea.2019.8833888

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Jia et al. [14] considered factors such as power supplies, power factor, total harmonic distortion, degree of three‐phase unbalance, load shape factor and other factors combined with LSTM to predict line loss. Li et al.…”

Section: Introductionmentioning

confidence: 99%

“…Jarkko et al [13] considered historical line loss and climate data to predict line loss using long short-term memory (LSTM) network. Jia et al [14] considered factors such as power supplies, power factor, total harmonic distortion, degree of three-phase unbalance, load shape factor and other factors combined with LSTM to predict line loss. Li et al [15] used the K-Means clustering method to classify the parameters affecting the line loss in the distribution network.…”

Section: Introductionmentioning

confidence: 99%

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Prediction method of line loss rate in low‐voltage distribution network based on multi‐dimensional information matrix and dimensional attention mechanism‐long‐and short‐term time‐series network

Zhang

Hui

et al. 2022

IET Generation Trans & Dist

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Line loss accounts for most of the energy loss in low‐voltage distribution network. Accurate prediction of line loss rate (LLR) is of great significance to eliminate abnormal transmission line faults in time and ensure power supply safety. The existing prediction methods for LLR seldom consider the seasonal trend data that affect it, and there is a hysteresis effect in predicting non‐stationary line loss sequences. To solve the above problems, this paper proposes a prediction method based on multi‐dimensional information matrix and dimensional attention mechanism‐long‐and short‐term time‐series network (DAM‐LSTNet). Firstly, the maximum information coefficient (MIC) method is used to screen the distribution network characteristics and seasonal trend parameters. Secondly, the variational mode decomposition (VMD) method optimized by the genetic algorithm is used to decompose the historical line loss data and form a multi‐dimensional information matrix with the screened characteristic parameters of the network. Finally, the multi‐dimensional information matrix is put into the LSTNet network with dimensional attention mechanism to predict the LLR. The example analysis shows that compared with the existing methods, it has the advantages of sufficient consideration of prediction parameters, adaptive changes of prediction weights, weak hysteresis effect and high prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%