scope:

An accepted Heating, Ventilation and Air-conditioning (HVAC) energy consumption model is a necessary step for various applications including fault detection and diagnostics, measurement and verification in building retrofit. It is common to use a polynomial regression to decouple the baseload from total building energy consumption while considering the baseload as a fixed value. To improve the decoupling algorithm, Fourier series is introduced to represent the dynamic baseload. Furthermore, a probabilistic graphical Bayesian network model with discrete and continuous variables is developed to predict the HVAC energy consumption. Sub-metering data from a four-story university dormitory is used to test the proposed Fourier series based decomposition and Bayesian Network based predictions. The results indicate that polynomial regression integrated Fourier series decomposition method is feasible and has a more accurate performance. Using the decomposed data, the HVAC system electricity energy consumption is predicted using a Bayesian network. The preliminary results suggested that the Bayesian network is a time-saving and accurate prediction model based on the ASHRAE Guideline 14 recommended metrics.