Deep-learning-based demand response optimization and prediction model

With the continuous increase in electricity demand, power grids face challenges such as peak load regulation, frequency adjustments, and power shortages. Demand-side response (DR) has emerged as an effective tool to balance electricity supply and demand by controlling user-side resources. This study proposes a deep learning-based prediction and optimization model to enhance the accuracy and effectiveness of DR. Using a convolutional neural network (CNN) and long short-term memory network (LSTM), we perform detailed load forecasting and extract resource parameters. Dynamic parameter identification enables online extraction and optimal management of these parameters. Experimental results demonstrate significant improvements in load forecasting accuracy and system reliability. Key contributions include integrating deep learning to model complex factors and resource characteristics, developing a CNN-LSTM-based load forecasting model, and creating a dynamic priority control algorithm for smart appliances based on the comfort index (KApp). This research supports stable power system operation and has significant theoretical and practical value.