Power system short-term load prediction method based on time convolution network

Abstract

The invention provides a power system short-term load prediction method based on a time convolution network, and the method comprises the steps: collecting historical load data, and carrying out the preprocessing of the data; constructing causal expansion convolution models, and respectively inputting the preprocessed data into two different causal expansion convolution models for convolution processing; connecting the two results processed by the causal expansion convolution model to form a residual block; stacking the residual blocks to obtain a time convolution network; and carrying out full convolution layer calculation by using a time convolution network to predict a future power load demand. The invention provides a short-term load prediction method for a power system. A convolutionmodel is expanded through causality; causal convolution processing and extended convolution processing are carried out on the data, then residual convolution processing is carried out, learning objectives and difficulty are simplified, and finally full convolution layer calculation is carried out by using the time convolution network, so that the time and hardware requirements required in the prediction process are reduced, and the precision is equivalent to that of a mainstream recurrent neural network.

Description

technical field [0001] The present invention relates to the technical field of electric power system and automation, and more specifically, relates to a short-term load forecasting method of electric power system based on temporal convolution network. Background technique [0002] Short-term power load is a periodic non-stationary stochastic process, which has seasonal changes, periodic changes by week and hour, and differences between holidays and normal working days. In short, short-term power load forecasting is a nonlinear mapping sequence modeling problem related to various factors, and the output sequence is predicted from the input sequence. [0003] In general, recurrent neural networks (RNN) are considered the default configuration for sequence modeling, and even Ian Goodfellow uses "Sequence Modeling: Recurrent and Recurrent Networks" as the chapter title in the book "Deep Learning", which indicates that sequence modeling There is a very close connection with loop...

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Problems solved by technology

[0005] The present invention provides a short-term load forecasting method for power systems based on temporal convolutional networks to overcome the technic...

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