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Based on the investigation to 59 residential buildings in China, this study establishes the prediction model of annual energy consumption of residential buidlings using four different modeling methods such as support vector machine (SVM), traditional back propagation neural network (BPNN), radial basis function neural network (RBFNN) and general regression neural network (GRNN). The simulation results...
Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Many forecasting approaches such as artificial neural network (ANN), support vector machine (SVM), autoregressive integrated moving average (ARIMA) and grey model, have been proposed in the field of building cooling load prediction. However, none of them has enough accuracy...
A number of different forecasting methods have been proposed for cooling load forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN), but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to improve time efficiency of prediction, a new hourly...
The methodology to predict building energy consumption is increasingly important for building energy baseline model development and measurement and verification protocol (MVP). Improving the energy efficiency of buildings by examining their heating, ventilating, and air-conditioning (HVAC) systems represents an opportunity. To improve energy efficiency, to increase occupant comfort, and to provide...
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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