Power grid load prediction method based on depth LSTM neural network

A power grid load and neural network technology, applied in forecasting, instrumentation, data processing applications, etc., can solve the problems of low power grid load forecasting accuracy and short time steps, so as to avoid calculation and time-consuming forecasting, improve real-time performance, The effect of ensuring integrity

Inactive Publication Date: 2018-08-17
CHINA UNIV OF MINING & TECH
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[0005] The present invention aims at at least to a certain extent to solve technical...

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  • Power grid load prediction method based on depth LSTM neural network
  • Power grid load prediction method based on depth LSTM neural network
  • Power grid load prediction method based on depth LSTM neural network

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[0036] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0037] The power grid load forecasting method based on the deep LSTM neural network according to the embodiment of the present invention will be described below with reference to the accompanying drawings.

[0038] figure 1 It is a flowchart of a power grid load forecasting method based on a deep LSTM neural network according to an embodiment of the present invention.

[0039] Such as figure 1 As shown, the power grid load forecasting method based on the deep LSTM neural network of the embodiment of the present invention includ...

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Abstract

The invention discloses a power grid load prediction method based on a depth LSTM neural network, and the prediction precision, step length and the real-time performance can be improved. The method comprise the steps of generating a training sample according to input characteristic data and the load data, wherein The input characteristic data comprises meteorological information of experimental time and time type information of whether it is working days, processing the training samples, training the processed training samples through the LSTM neural network to obtain an LSTM prediction model,inputting the meteorological information of experimental time and time type information of whether it is working days, into the LSTM prediction model to predict the power grid load in the to-be-predicted time so as to obtain a power grid load prediction result, analyzing the power grid load prediction result to determine whether the power grid load prediction result meets the accuracy requirement; if the accuracy requirement is not met, obtaining new training samples, and carrying out supplementary training on the LSTM prediction model through the new training samples so as to update the LSTMprediction model.

Description

technical field [0001] The invention relates to the technical field of grid load forecasting, in particular to a grid load forecasting method based on a deep LSTM neural network. Background technique [0002] Forecasting the grid load is the key to ensure the safe and reliable operation of the grid and reduce the economic loss of the grid. Improving the accuracy of load forecasting has been the focus of people's research for many years. Due to the variety of energy sources in the power grid and the different energy utilization methods, the load data of the power grid is highly volatile and random, resulting in low load forecasting accuracy, and it is difficult to accurately fit the distribution of load data. [0003] There are currently many forecasting methods for load forecasting. However, with the continuous acceleration of power grid intelligence, the increase in data volume and the volatility and randomness of data make traditional load forecasting methods more and more...

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Application Information

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IPC IPC(8): G06Q50/06G06Q10/04
CPCG06Q10/04G06Q50/06Y04S10/50
Inventor 孙晓燕邵辉赵琳汪敬人暴琳耿聪李玉柱
Owner CHINA UNIV OF MINING & TECH
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