Research on the Optimization Algorithm of Electric Energy Allocation Based on Deep Learning in Smart Grid
With the continuous expansion of domestic power grid scale,with the help of cutting-edge communication technology,smart grid can realize real-time monitoring of power resources,thereby improving the efficiency of equipment use.In order to deepen the intelligence of power distribution,deep learning technology,especially Long Short-Term Memory(LSTM)network,is added to capture the hidden long-term dependence in power demand data and accurately predict the future power demand trend.With the support of high-performance computing environment,massive historical power demand data are deeply analyzed by using TensorFlow and Keras framework.In order to ensure the data quality,the original data is strictly preprocessed and a high-quality data set suitable for deep learning model is constructed.Then,according to the scientific proportion,the data set is divided into training set,verification set and test set in order to train and optimize the deep learning model.The experimental results show the forecast error of LSTM network on the test set is as low as 0.028,and the prediction accuracy is as high as 95.4%,which is superior to the traditional Recurrent Neural Network(RNN)and Convolutional Neural Network(CNN)models.This method is helpful to improve the operation efficiency of power system and provide strong support for the further development of smart grid.
smart griddeep learningoptimization algorithm for electric energy allocation
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