Research on Building Energy-Saving Prediction Model of Building Air Conditioning Based on Deep Learning

Aiming at the problem of high energy consumption of building air conditioning system, this paper proposes an energy-saving prediction model construction system based on deep learning. The system uses the powerful feature extraction capability of deep learning algorithm and the running data of buildings to build a high-precision energy consumption prediction model. Through the analysis of many historical energy consumption data, the system can learn the complex relationship between building air conditioning energy consumption, environmental variables, and equipment status, and achieve accurate prediction of future energy consumption, to provide a scientific basis for the formulation of energy conservation strategies. In the algorithm design, this paper introduces the adaptive learning mechanism, so that the model can automatically adjust the prediction strategy according to the change of the environment, and improve the prediction accuracy and stability. In addition, the energy saving effect of the model under different working conditions is verified by energy consumption simulation experiments. The results show that the system can effectively reduce the energy consumption of the building air conditioning system while maintaining the indoor environment comfort.