Power distribution system network loss prediction method based on long-term and short-term memory network

Abstract

The invention discloses a power distribution system network loss prediction method based on a long-term and short-term memory network, and the method comprises the steps: obtaining a plurality of feature quantities of a prediction region at all moments, and constructing a time sequence of each feature quantity; obtaining a reference sequence and a comparison sequence from each characteristic quantity time sequence, calculating the correlation degree between the reference sequence and the comparison sequence by adopting a grey correlation analysis method, and selecting an influence factor corresponding to the optimal comparison sequence as an input influence factor; obtaining a training sample from each characteristic quantity time sequence according to the input influence factor; establishing a long-term and short-term memory network model, and training the long-term and short-term memory network model by using the training sample to obtain a network loss prediction model; and obtaining an input influence factor of the network loss value at the prediction moment, and performing prediction by using the network loss prediction model to obtain the network loss value at the predictionmoment. The method can improve the prediction precision and efficiency of the network loss of the power distribution system, so as to achieve the purposes of guiding the energy-saving work more efficiently and determining the energy-saving amount of a project.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a method for predicting network loss of a power distribution system based on a long-short-term memory network. Background technique [0002] With the development of social economy, the scale of my country's distribution network is becoming larger and larger, and the number of power equipment is increasing. The power loss of 10kV distribution network has accounted for more than 20% of the entire power network, and there is a large space for loss reduction. In recent years, the automation level of the distribution network has been continuously improved, and various automatic data acquisition systems have emerged continuously, which provides more sufficient data for the calculation of the loss of the distribution network, but also brings great challenges. [0003] The existing traditional calculation methods tend to focus on the calculation of line loss, and cannot dig out the...

AI Technical Summary

Problems solved by technology

[0003] The existing traditional calculation methods tend to focus on the calculation of line loss, and cannot dig out the relationship between massive data and power grid loss. For different 10kV distribution networks, they cannot accurately identify the main factor sets that affect line loss

Therefore, more and more artificial intelligence a

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