Electricity Price Prediction Based On Hybrid Model Of Adam Optimized LSTM Neural Network And Wavelet Transform

In modern society,electricity is closely related to human life and economic development.Therefore,how to use electric energy reasonably and efficiently has become a closely concerned issue of a country and the world.In this context,electricity price forecasting has been one of the main efforts of energy market researchers and power decision makers,they can arrange or adjust power production and implement relevant decision-making in real time through forecasting electricity prices.However,to a large extent,the price of electricity is highly unstable,which can be affected by factors such as seasons,fuels and weather.These factors will cause the fluctuations of electricity price in real time,so the prediction of electricity price is a difficult task for researchers.In recent years,with the rapid development of artificial intelligence,deep learning has been favored by researchers in various fields due to its outstanding performance.The Recurrent Neural Network(RNN)is a type of deep learning model that performs well on processing sequence data.What’s more,Long Short-Term Memory Network(LSTM)can further improve the accuracy of prediction,which has made a series of improvements on the basis of RNN.An optimizer based on stochastic gradient optimization,Adam,can make the deep learning model perform better when dealing with nonlinear problems.At the same time,as a kind of transform analysis method,wavelet transform is a powerful tool for signal time-frequency analysis.It can decompose time series data to make the processed data variance smaller without changing the data itself.Therefore,this paper proposed a new hybrid model combining Adam optimized LSTM neural network and wavelet transform,denoted as WT-Adam-LSTM,which has outstanding performance in electricity price forecasting.This study listed four cases to demonstrate the superiority of the hybrid model,and the electricity data from New South Wales of Australia and France are adopted to illustrate the excellence of WT-Adam-LSTM.The final experimental results show that the proposed model can significantly improve the accuracy of electricity price prediction.