Power Load Forecasting Based On CS-LSTM Algorithm

Power load forecasting is the foundation of the construction and operation of power grids.According to the predicted results,it can better guide the power system dispatching and safety monitoring work.Therefore,it is particularly important to ensure the accuracy of the predicted results.This paper uses BP(Back Propagation)neural network and Long short-term memory(Long short-term memory,LSTM)neural network to predict the power load,and then optimizes the hyperparameters of the LSTM neural network through the Cuckoo Search(CS)algorithm,Use the adjusted model to perform model training and testing,and finally make a comparison.First of all,this article uses python to preprocess the selected data,divide the selected data into training set and test set,use BP and LSTM neural networks to train and test the data respectively,and evaluate the pros and cons of the model through different indicators;Secondly,in order to improve the accuracy of the neural network,the cuckoo search algorithm is used to optimize the number of iterations of the LSTM neural network,the learning rate and the number of nodes in the hidden layer,and then make predictions,compare the results of different models,analyze the results,and get in conclusion.