<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Yi Jiang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stochastic weather model for building HVAC systems</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1995</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">521-532</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The weather is a multi-dimensional stochastic process; the traditional typical or standard meteorological year is not enough to describe the random behaviour of weather. The model presented in this paper is based on the vector auto-regressive (VAR) time series method. From the validation results, it can be seen that the stochastic weather model is essential to describe real climate behaviour, and the accuracy obtained is sufficient for the application of the stochastic weather model in the simulation and stochastic analysis of building HVAC systems.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><work-type><style face="normal" font="default" size="100%">Research Article</style></work-type></record></records></xml>