Optimization and research of smart grid load forecasting model based on deep learning

In order to accurately grasp the power consumption of the market and deeply understand the operation law of the power market, the power grid must achieve accurate power load prediction by studying the supply and demand of the power system and the change law. Therefore, a smart grid load prediction analysis method based on deep learning is proposed. Firstly, DenseNet network is used to extract features, so as to achieve feature reuse and improve efficiency. Then, combining the long short-term memory (LSTM) network based on attention mechanism, a combined model DenseNet–AM–LSTM is proposed to solve the long-term dependence problem of feature extraction. After using batch normalization, the model can use higher learning rate to improve the training speed of the model and obtain the best prediction accuracy of the model. The experimental results show that this method can accurately predict the power load, and the accuracy is higher than the current popular methods, and it is expected to become an important technical support for solving the core problems of smart grid.