Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

Building sector account for significant global energy consumption and Heating Ventilation Air Conditioning (HVAC) systems contribute to the highest portion of building consumption. Therefore, potential saving by improving efficiency HVAC is huge various fault detection diagnosis (FDD) methods have been studied this purpose. Although amongst all types existing FDD methods, data-driven based ones are regarded as most effective methods. As a relatively new branch approaches, deep learning (DL) shown promising results, comprehensive review DL applications in area absent. To fill research gap, systematic with meta analysis analyses relevant studies both quantitatively qualitatively. The conducted searching Web Science, ScienceDirect, Semantic search. There 47 eligible included following preferred reporting items reviews meta-analyses (PRISMA) protocol. 6 out identified effectiveness FDD. used method 2D convolutional neural network (CNN). Results suggest that show results However, use simulation/lab experiment data real-world complexities not fully investigated. need be further tested scenarios support decision-making.