Investigation on Energy Data Prediction and Optimization Methods Based on Deep Learning

Energy data prediction and optimization play an important role in achieving sustainable energy development and improving energy utilization efficiency. Deep learning technology, as a powerful data-driven method, has received widespread attention in the field of energy. However, there are still certain shortcomings in the research of energy data prediction and optimization methods based on deep learning. This study explored energy data prediction and optimization methods based on deep learning by reviewing relevant literature, analyzing the characteristics of deep learning models and optimization algorithms, and conducting experimental validation. Deep learning models including multi-layer perceptrons, recurrent neural networks, and convolutional neural networks were used in the study. By combining feature engineering and data preprocessing methods, energy data was predicted and optimized. The research results indicated that the Mean Squared Error (MSE) range of this method was 0.02-0.06, and the Mean Absolute Percentage Error (MAPE) range was 1%-6%. Deep learning based energy data prediction and optimization methods had significant potential. A suitable deep learning model could accurately predict energy data and improve prediction performance by capturing the time dependence of time series data.