Research on Building Energy Consumption Prediction and Optimal Control System Based on Deep Learning Algorithm

This paper studies the design of building energy consumption prediction and optimization control system using deep learning algorithm. First, this paper uses deep learning algorithms, especially recurrent neural networks (RNN), to capture the cyclic characteristics of building energy consumption data. RNNS and their variants Long Short-Term Memory Networks (LSTMS) perform well in processing time series data and can efficiently extract and utilize useful information from historical energy consumption data. A complete building energy consumption forecasting and control system is designed. In order to further improve the prediction accuracy, this paper also introduces the spectrum analysis method, through the frequency domain characteristics of energy consumption data to extract more hidden patterns and correlation information. In order to verify the effectiveness and reliability of the system, detailed simulation experiments are carried out in this paper. The experimental results show that the system has excellent performance in both prediction accuracy and control effect, and can significantly reduce building energy consumption and improve energy utilization efficiency. The design scheme of building energy consumption prediction and optimization control system based on deep learning algorithm proposed in this paper provides a new technical support and solution for realizing building energy saving and emission reduction.