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In order to find a fast and effective method to predict building energy consumption at different climate zones, this paper used artificial neural network (ANN) for this prediction with 20 input parameters, including 18 building envelope performance parameters, heating degree day (HDD) and cooling degree day (CDD). A backpropagation neural network has been preferred and the data have been presented...
Thermal load prediction is essential for optimal operations of heating, ventilation, and air conditioning (HVAC) systems. Usually, the building thermal load is predicted by using artificial neural network (ANN) model based on environmental input variables. Unfortunately, it is not obvious that how many the input items should be or what preprocessing of inputs are best, which can cause significant...
According to meteorological element data of test reference year (TRY), a dynamic simulation program calculates the hourly cooling loads of an office building from April to September. Then, a general Visual Basic program is developed based on the error back-propagation (BP) algorithm of artificial neural network (ANN). The network is trained and tested by the obtained data. The results are presented...
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