Predication of smart building energy consumption based on deep learning algorithm

Since smart cities have received extensive attention in recent years, and there is no more research data on energy consumption in smart cities. In order to improve the energy consumption prediction accuracy of intelligent buildings, a building energy consumption prediction method based on deep learning algorithm is proposed. By predicting the power consumption, we can analyze whether the energy consumption of the building is reasonable, so as to make further management actions. First of all, the specifies the overall data processing system by using the method of cloud computing, and the overall data is stored and calculated by means of cloud computing. In order to verify the effectiveness of the algorithm in this paper, the algorithm in this paper is applied to commercial buildings, and the data is compared with other algorithms. The results show that, whether compared with the data regression model or with other learning methods, the algorithm in this paper has obvious advantages in prediction accuracy and stability, and can be used to predict the energy consumption of buildings.