State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Online support vector machine application for model based fault detection and isolation of HVAC system

Abstract—Preventive maintenance plays an important role in Heating, Ventilation and Air Conditioning (HVAC) system. One cost effective strategy is the development of analytic fault detection and isolation (FDI) module by online monitoring the key variables of HAVC systems. This paper investigates realtime FDI for HAVC system by using online Support Vector Machine (SVM), by which we are able to train a FDI system with manageable complexity under real time working conditions. It is also proposed a new approach which allows us to detect unknown faults and updating the classifier by using these previously unknown faults. Based on the proposed approach, a semi unsupervised fault detection methodology has been developed for HVAC system

Fault Detection and Diagnosis Encyclopedia for Building Systems:A Systematic Review

This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository

Investigation of the Prevalence of Faults in the Heating, Ventilation, and Air-Conditioning Systems of Commercial Buildings

This dissertation describes a large-scale investigation of heating, ventilation, and air-conditioning (HVAC) fault prevalence in commercial buildings in the United States. A multi-year dataset with 36,556 pieces of HVAC equipment including air handling units (AHUs), air terminal units (ATUs), and packaged rooftop units (RTUs) was analyzed to determine values for several HVAC fault prevalence metrics. The primary source of data for this study comes from three commercial fault detection and diagnostics (FDD) providers. Since each FDD provider uses different terms to refer to the same fault in an HVAC system, a mapping function was created for each FDD provider’s dataset, to convert the fault reports to a single standardized fault identifier. The fault identifier is taken from a standard taxonomy that was created for this purpose. Since the commercial FDD software outputs are inherently subject to some level of error, i.e., they could have false negatives and false positives, a field study was conducted to gain greater insight into the commercial FDD software results. Two buildings from among the buildings of one of the FDD providers were selected. The RTUs serving these two buildings were monitored for about two weeks using our installed data loggers. The actual faults in these buildings were identified using methods that we developed or selected from the literature. The results of the field study were compared with the FDD provider fault reports. This study also proposes a data-driven FDD strategy for RTUs, using machine learning classification methods. The FDD task is formulated as a multi-class classification problem. Seven typical RTU faults are discriminated against one another as well as the normal condition. Nine classification methods were applied to a dataset of simulation data, which was split into a training set and a test set. The performance of the classifiers for individual faults was characterized using true positive rate and false positive rate statistical measures. The relative importance of input variables was analyzed, and is also discussed in the dissertation. Advisor: David Yuil

Fault Diagnosis of Reciprocating Compressors Using Revelance Vector Machines with A Genetic Algorithm Based on Vibration Data

This paper focuses on the development of an advanced fault classifier for monitoring reciprocating compressors (RC) based on vibration signals. Many feature parameters can be used for fault diagnosis, here the classifier is developed based on a relevance vector machine (RVM) which is optimized with genetic algorithms (GA) so determining a more effective subset of the parameters. Both a one-against-one scheme based RVM and a multiclass multi-kernel relevance vector machine (mRVM) have been evaluated to identify a more effective method for implementing the multiclass fault classification for the compressor. The accuracy of both techniques is discussed correspondingly to determine an optimal fault classifier which can correlate with the physical mechanisms underlying the features. The results show that the models perform well, the classification accuracy rate being up to 97% for both algorithms

Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems

Globally, the buildings sector accounts for 30% of the energy consumption and more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and Air Conditioning (HVAC) system is the most extensively operated component and it is responsible alone for 40% of the final building energy usage. HVAC systems are used to provide healthy and comfortable indoor conditions, and their main objective is to maintain the thermal comfort of occupants with minimum energy usage. HVAC systems include a considerable number of sensors, controlled actuators, and other components. They are at risk of malfunctioning or failure resulting in reduced efficiency, potential interference with the execution of supervision schemes, and equipment deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve their reliability, efficiency, and performance, and to provide preventive maintenance. In this thesis work, two neural network-based methods are proposed for sensor and actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised sensor data validation and fault diagnosis method using an Auto-Associative Neural Network (AANN) is developed. The method is based on the implementation of Nonlinear Principal Component Analysis (NPCA) using a Back-Propagation Neural Network (BPNN) and it demonstrates notable capability in sensor fault and inaccuracy correction, measurement noise reduction, missing sensor data replacement, and in both single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks (CNNs) is developed for single actuator faults. It is based a data transformation in which the 1-dimensional data are configured into a 2-dimensional representation without the use of advanced signal processing techniques. The CNN-based actuator fault diagnosis approach demonstrates improved performance capability compared with the commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and standard Neural Networks). The presented schemes are compared with other commonly used HVAC fault diagnosis methods for benchmarking and they are proven to be superior, effective, accurate, and reliable. The proposed approaches can be applied to large-scale buildings with additional zones

Data-driven remote fault detection and diagnosis of HVAC terminal units using machine learning techniques

The modernising and retrofitting of older buildings has created a drive to install building management systems (BMS) aimed to assist building managers pave the way towards smarter energy use, improve maintenance and increase occupants comfort inside a building. BMS is a computerised control system that controls and monitors a building’s equipment, services such as lighting, ventilation, power systems, fire and security systems, etc. Buildings are becoming more and more complex environments and energy consumption has globally increased to 40% in the past decades. Still, there is no generalised solution or standardisation method available to maintain and handle a building’s energy consumption. Thus this research aims to discover an intelligent solution for the building’s electrical and mechanical units that consume the most power. Indeed, remote control and monitoring of Heating, Ventilation and Air-Conditioning (HVAC) units based on the received information through the thousands of sensors and actuators, is a crucial task in BMS. Thus, it is a foremost task to identify faulty units automatically to optimise running and energy usage. Therefore, a comprehensive analysis on HVAC data and the development of computational intelligent methods for automatic fault detection and diagnosis is been presented here for a period of July 2015 to October 2015 on a real commercial building in London. This study mainly investigated one of the HVAC sub-units namely Fan-coil unit’s terminal unit (TU). It comprises of the three stages: data collection, pre-processing, and machine learning. Further to the aspects of machine learning algorithms for TU behaviour identification by employing unsupervised, supervised, and semi-supervised learning algorithms and their combination was employed to make an automatic intelligent solution for building services. The accuracy of these employed algorithms have been measured in both training and testing phases, results compared with different suitable algorithms, and validated through statistical measures. This research provides an intelligent solution for the real time prediction through the development of an effective automatic fault detection and diagnosis system creating a smarter way to handle the BMS data for energy optimisation