An application of Bayesian Network approach for selecting energy efficient HVAC systems

The conventional approach of selecting HVAC systems is based on a designer's knowledge and experience, and this may lead to flawed decisions. The ever-growing accumulation of building performance data makes the machine learning algorithm based HVAC system selection possible. This study presents an innovative approach wherein the Bayesian Network technique is applied to select the most energy efficient primary HVAC systems. The database upon which the approach is developed is the 2012 Commercial Building Energy Consumption Survey (CBECS). The first step of this research involves clustering a group of similar buildings for the target building. Euclidean distance is adopted to calculate the similarly of a building with the target building. In the meantime, a survey is carried out to investigate the major factors that designers considered when selecting the primary HVAC system. The survey results show that climate zone, design cooling/heating load, and principal activity type are three main factors considered by designers and only 13% designers value the building energy consumption. In this study, all factors that could potentially influence the HVAC system's energy consumption are used to construct the directed acyclic graph of the proposed Bayesian Network Classifier. This classifier is trained with high energy efficient buildings data after the filtering out of irrational outliers. Then, the trained classifier is applied to select the primary cooling systems for three case buildings. The results indicate that the selected systems coincide with common HVAC design logic. The proposed method provides designers with an innovative approach to select energy efficient HVAC systems by using the data of hundreds of high energy efficient buildings. This study demonstrates the feasibility and capability of data-driven building design.