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Research on hybrid variable weight prediction model of line loss in transformer district Based on CEEMDAN-WPT

Line loss rate is an important economic indicator for evaluating the operation of power grid enterprises. In order to improve the prediction accuracy of the line loss in the station area, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), wavelet packet threshold denoising (WPT), genetic algorithm (GA), generalized regression neural network (GRNN) and gated recurrent unit (GRU) platform line loss variable weighted hybrid model (VWHM). First, use EEMDAN to decompose the electrical characteristic parameters related to the time series into multiple different subsequences, respectively calculate the amplitude variance of the IMF component through Fourier analysis, and combine the WPT to reduce the noise of the high-frequency signal containing noise to extract the representative Variables are used as predictive inputs. Finally, using the global optimization ability of GA, the weight coefficients of the combined prediction model composed of GRNN and GRU are used to solve the sample moving adaptive variable weights, and weighted to obtain the final prediction result. The simulation results show that the proposed variable weight combination model has higher prediction accuracy than the single model and the traditional VWHM, and is superior in line loss prediction.