Power distribution transformer area electricity sales accurate prediction method based on modal GRU learning network

Abstract

The invention discloses a power distribution transformer area electricity sales accurate prediction method based on a modal GRU learning network, which comprises the following steps of: S1, obtaininghistorical data of electricity sales of a power distribution transformer area, and dividing the historical data into a test set and a training set; S2, preprocessing the data, complementing the sampling time points to ensure continuity of the sampling time points, and filling up missing data of the sampling points by utilizing an average interpolation method; S3, determining an optimal modal number K of variational mode decomposition (VMD) according to the center frequency of each modal component by using an experimental method; S4, carrying out VMD decomposition on the historical data of theelectricity sales of the transformer area, and respectively extracting a decomposed low-frequency modal component and a decomposed high-frequency modal component; S5, predicting a low-frequency mode and a high-frequency mode respectively by using a Prophet prediction model and a GRU learning network; and S6, reconstructing the prediction result of each mode, and obtaining a predicted value of theelectricity sales of the transformer area. The method can improve the prediction precision of the electricity sales of the transformer area, and can provide theoretical and practical support for the precise prediction and management of the electricity sales of the transformer area.

Description

technical field [0001] The invention relates to the field of electricity forecasting, in particular to a method for accurately predicting electricity sales in distribution stations based on a modal GRU learning network. Background technique [0002] The analysis and prediction of electricity sales in the Taiwan area can help power supply companies adjust power supply plans and optimize power supply structure, which is in line with the development concept of building a conservation-oriented society and promoting energy conservation and emission reduction. Therefore, the establishment of an effective forecasting model for electricity sales in Taiwan has always been a research hotspot in the field of electric power. [0003] The electricity sales in the station area are usually affected by the superposition of various factors such as user behavior, load changes, seasonal changes, holidays, etc., resulting in an unstable change trend in its time series. Commonly used forecasting...

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Problems solved by technology

[0003] The electricity sales in the station area are usually affected by the superposition of various factors such as user behavior, load changes, seasonal changes, holidays, etc., resulting in an unstable change trend in its time series. Commonly used forecasting models include: support vector machine, stochastic Forest algorithm and neural network, etc., but due to the lack of reasonable detailed decomposition of the power data, the influence of multiple superimposed factors on the power data has not been considered, so the prediction effect is usually not good

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