TY - JOUR
TI - Real time prediction method of energy consumption of geothermal system in public buildings based on wavelet neural network
AU - Li Yongguang
AU - Guo Shaocui
JN - Thermal Science
PY - 2022
VL - 26
IS - 3
SP - 2373
EP - 2384
PT - Article
AB - Real time prediction of energy consumption is the basis of energy  conservation and emission reduction. Aiming at the problems of large  prediction error and poor effect, a real-time prediction method of energy  consumption of geothermal system of public buildings based on wavelet neural  network is proposed. Firstly, the energy consumption of geothermal system  in public buildings is analyzed, the wavelet neural network is designed, the  neural network is optimized and solved by genetic algorithm, and the  necessity of constructing the real-time prediction model of energy  consumption based on wavelet neural network is established. Then it  introduces the basic principle of model establishment, wavelet analysis, and  shows the role of wavelet analysis in prediction model. Finally, based on  the distribution structure of public buildings, this paper analyzes the  energy consumption system of geothermal system, constructs the energy  consumption prediction method, analyzes the over­all temperature regulation  energy consumption prediction principle of building geothermal system, and  realizes the real-time prediction of energy consumption of geothermal system  of public buildings. The experimental results show that the energy  consumption real-time prediction results of the designed method are  basically similar to the actual prediction values, and the prediction  efficiency is high, which can effectively reduce the energy consumption of  the geothermal system of public buildings.