Open AccessResearch on High-rise Building Energy Saving Based on Data Fusion Model
Author(s) -
Zhenwei Chen,
Yang Zijing
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/580/1/012007
Subject(s) - particle swarm optimization , electricity , computer science , nonlinear system , power consumption , mathematical optimization , power (physics) , consumption (sociology) , high rise , algorithm , engineering , mathematics , physics , structural engineering , quantum mechanics , electrical engineering , social science , sociology
With the economic development, high-rise building projects are popularized in more real estate development. The different electricity types of high-rise buildings are separately predicted in the paper to better discover the laws of each electricity type, which is more accurate than using only the total power consumption to make predictions, so that the algorithm will have stronger nonlinear fitting ability. Moreover, by using optimized particle swarm optimization algorithm to solve the fusion model parameters among multiple types of data, the parameters of the WLSSVM model are optimized to find the optimal combination of parameters, which effectively improves the prediction accuracy of the model and contributes to the energy saving and emission reduction of high-rise buildings.