Power grid load prediction method based on depth LSTM neural network

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...

AI Technical Summary

Problems solved by technology

[0005] The present invention aims at at least to a certain extent to solve technical problems such as insufficient accuracy of power grid load forecasting and short time steps

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