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.

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. [ABSTRACT FROM AUTHOR]