SET Proceedings 2978-4387 Caravel Press 10.65582/set.2010.106 2010.106 Space cooling load prediction for building using online LS-SVM ZhangJiangtao CaoShuanghua Canoo@usst.edu.cn LiuFang School of Environment and Architecture, University of Shanghai for Science & Technology Shanghai, China 24082010 9 978-1-995850-04-7 1 7 24 August 2010 © 2010 Copyright WSSET 2010

Space cooling load affected by lots of factors is transient, multi-dimensional and highly interactive. A model of Online Least Squares Support Vector Machine (LS-SVM) is established to forecast the space cooling load of building, and the correlation analysis is used to find out the main parameters of influencing factors, such as, dry-bulb temperature, solar irradiance, and so on. As an example, the hourly space cooling load of an office building in shanghai is investigated. The hourly space cooling load is firstly calculated by the simulation software of DEST, and then, an online LS-SVM model is presented to forecast the load. The simulation results show that the online LS-SVM model is effective for space cooling load prediction.

 space cooling load prediction online Least Squares Support Vector Machine 24 to 27 August 2010 9th International Conference on Sustainable Energy Technologies SET2010 9 Shanghai, China