Research On Building Energy Consumption Based Upon The Difference Between The New And The Original Lighting Standard

With the rapid development of social life and residential buildings, people’s requirement for indoor comfort has becoming much more higher, making full use of natural light and creating suitable lighting environment are of great significance. In China, design experience was usually being used to determine the size of outside windows at the preliminary design stage. Since the new version “Standard for daylighting design of buildings”GB50033 has fully revised the original standard, which uses the average value of daylight factor in the stead of the minimum value of daylight factor. This revise will lead to the change of window size and eventually change the consumption of building energy. All of these show that determine the size of outside windows which only depends on design experience is still not comprehensive and accurate enough.Most of the previous researches on daylight environment and building energy saving were on the basic of single version of “Standard for daylighting design of buildings”, while the effect of different daylight evaluation indexes was not took into account, so the building performance can not be evaluated. Based on the issues above, the author tries to carry out the study from 3 perspectives listed below:Firstly, a residential and office building model respectively in Shenyang, Beijing, Changsha, Guangzhou and Kunming have been built to determine the lower limit of outside window size. In addition, the changing rule of window size has been analyzed.Secondly, the residential and office building models which meet the lighting evaluation indexes were established by DesignBuilder software, and the influences of daylight evaluation indexes and equipment operation modes on the building energy consumption were analyzed.Finally, the predicted model of exterior window size was established by Back Propagation neural network, which based on using the orthogonal experimental design method to get training data. The result shows that the predicted model could predict the exterior window size according to the dimension of building envelopes. This predicted model provided a new method for exterior window design at the preliminary stage, which has great significance for building design and energy saving.