Single Factor Predictive Analysis of Power Consumption Based on Deep Learning

The topic of this paper is power system power consumption prediction based on BP neural network and LSTM neural network. In power systems, accurate prediction of power consumption is very important to help energy companies to perform refined energy management and avoid energy wastage and unnecessary capital expenditure. The research in this paper proposes a deep learning-based power consumption prediction model that combines BP neural networks and LSTM neural networks with high accuracy and robustness by training and testing on real data. Also, common machine learning algorithms are investigated in this paper, and the experimental results are analyzed and discussed. In the parameter tuning and result analysis sections, we provide detailed optimization and analysis of the model, and provide ideas for further optimization of the model. The results of this paper show that the power consumption prediction models based on BP neural networks and LSTM neural networks provide a new solution for power consumption prediction in power systems and are expected to be widely used in practical applications.