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

Review and evaluation of methods in transport mode detection based on GPS tracking data

Mobility data,based on global positioning system(GPS)tracking,have been widely used in many areas,such as analyzing travel patterns,investigating transport safety and efficiency,and evaluating travel impacts.Transport modes are essential factors in understanding mobility within the transport system.Therefore,in this study,a significant number of algorithms were tested for transport mode detection.However,no conclusive recommendations can be drawn regarding which method should be used.The evaluation of the performance of the algorithms was not discussed systematically either in current literature.This paper aims to provide an in-depth review of the methods applied in transport mode detection based on GPS tracking data.The performances of the reviewed methods are then compared and evaluated to provide guidance in choosing algorithms for transport mode detection based on GPS tracking data.The results indicate that the majority of current studies are based on a supervised learning method for transport mode detection.Many of the reviewed methods first require manual dataset labeling,which can produce major drawbacks,such as inefficiency and human errors.It was also found that deep learning approaches have the potential to deal with large amounts of unlabeled raw GPS datasets and increase the accuracy and efficiency of transport mode detection.