01011nas a2200145   4500008004100000245005500041210005500096260000900151300001200160490000700172520056700179100001900746700001400765856008600779       1995                        eng d00aStochastic weather model for building HVAC systems0 aStochastic weather model for building HVAC systems  c1995  a521-5320 v303 a<p>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.</p>1 aHong, Tianzhen1 aJiang, Yi  uhttps://simulationresearch.lbl.gov/publications/stochastic-weather-model-building01511nas a2200145   4500008004100000245005400041210005400095260000900149300001200158490000700170520106600177100001401243700001901257856008901276       1993                        eng d00aStochastic analysis of building thermal processes0 aStochastic analysis of building thermal processes  c1993  a209-2180 v283 a<p>A methodology is presented for investigating the uncertainty properties of the building thermal processes caused by the random behaviour of the meteorological processes and the casual gains. A detailed building thermal model is used with a stochastic weather model and a random casual gain model. The probability distribution of the zone temperature of the building is calculated directly from these models. The overheating risk has been analysed as an example. The probability distribution of the periods when the zone temperature is higher than the demand temperature is calculated. The result shows all the possible situations rather than only a sample as would be obtained by running a normal simulation using given weather data. The influence of different building components on the overheating risk has been studied. The result shows that the most likely component for overheating risk in a residential building in Beijing is the window size. The thermal mass of the internal walls and the placing of windows have little effect on overheating risk.</p>1 aJiang, Yi1 aHong, Tianzhen  uhttps://simulationresearch.lbl.gov/publications/stochastic-analysis-building-thermal00408nas a2200109   4500008004100000245005700041210005700098260002100155100001400176700001900190856008900209       1993                        eng d00aStochastic analysis of overheating risk in buildings0 aStochastic analysis of overheating risk in buildings  aLondon, UKc19931 aJiang, Yi1 aHong, Tianzhen  uhttps://simulationresearch.lbl.gov/publications/stochastic-analysis-overheating-risk