This paper focuses on power demand-side response and orderly power consumption prediction

and proposes a hybrid model architecture based on deep learning. The power demand-side response guides users to adjust power consumption behavior and optimize resource allocation through incentive measures

and orderly power consumption prediction is essential to ensure the stable operation of the power grid. In the study

it combines long-term and short-term memory network (LSTM) and convolutional neural network (CNN)

uses LSTM to capture long-term dependence on time series data and the ability of CNN to extract spatial features

and further introduces bidirectional LSTM and attention mechanism to improve model performance. Through the experimental verification of historical electricity consumption data in a certain region

the model performs well in prediction accuracy

providing a new technical path for the efficient operation and energy management of the power system. Future research will explore the integration of deep learning with other technologies to further improve the accuracy and practicality of prediction.