Power load prediction method and device

Abstract

The invention discloses a power load prediction method and device. The method comprises the steps of inputting an original data set into a BP neural network, performing traversal search to obtain positions of missing data, outputting missing data values, and performing missing data filling to obtain a complete data set; performing parameter optimization on the complete data set, and outputting anoptimal hyper-parameter to generate an optimal hyper-parameter vector; constructing an LSTM neural network by using the optimal hyper-parameter vector, and inputting training data to train the LSTM neural network to obtain an LSTM model; inputting test data, and testing the LSTM model; inputting characteristic values into the LSTM model passing the test, and carrying out power load prediction, wherein the characteristic values comprise air temperature, humidity, wind speed, historical data and prediction time points. The method has the advantages that the time and resources are reduced, and the prediction precision and authenticity are improved.

Description

technical field [0001] The present invention relates to the field of electric load forecasting, and more particularly relates to a method and device for electric load forecasting. Background technique [0002] In the power system, there is simultaneity between grid power generation and power consumption. Therefore, the power grid management and dispatching department needs to make a power generation plan in advance. Therefore, power load forecasting has become an important research topic for power grid dispatching departments. The accuracy of Short Term Load Forecasting (STLF) is directly related to the operating costs of power companies. The change of electric load is random, but there is a certain regularity on the whole. Therefore, more and more scholars apply more intelligent nonlinear models to short-term load forecasting. Such as: long short-term memory artificial neural network (LSTM). [0003] LSTM is a special form of recurrent neural network (RNN). RNN is a t...

AI Technical Summary

Problems solved by technology

[0019] The technical problem to be solved by the present invention is that the prior art power load forecasting method consumes a lot of time and resources, and the forecasting accuracy and authenticity are not high.

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