Ensemble learning with member optimization for fault diagnosis of a building energy system

Han, Hongbin; Zhang, Zhan; Cui, Xiaoyu; Qin, Meng

doi:10.1016/j.enbuild.2020.110351

Cited by 62 publications

(17 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These two tables were inspired by the work in [104]. Examples of how the confusion matrix previously was used can be found in [96,104,105,111,150,155,156,158,160,161,163,168,170]. The variations in the performance evaluation metrics definitions are described in Table 11.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…Lastly, the fourth challenge requires a joint initiative, as standard metrics should be defined or developed. However, it can only be alleviated by authors providing the confusion matrix, as performed in [96,104,105,111,150,155,156,158,160,161,163,168,170]. This will allow other authors to calculate the metrics they need from the different articles, thus enabling better comparison.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…Performance Evaluation Metric Equation [96,104,105,111,150,155,156,158,160,161,163,168,170] Confusion matrix -Used in FD (1 nonfault class and 1 fault class) Global [104,105] Correct rate (CR) T N +∑ T P,ct N [159,165,169,190] False-diagnosis rate (FaDR) 1 − T N +∑ T P,ct N [94] Macro-F1 (MF1) [191] ∑…”

Section: Referencementioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Performance Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…As well as these advances in the machine learning area, which link well into meta-heuristic approaches, there is also scope for research into the combination of meta-heuristics with different optimisation algorithm approaches, leading to an overall more effective algorithm. In terms of algorithms research, the success of ensemble methods within the machine learning area (Abuassba et al, 2021;Shiue et al, 2021) indicates the potential of combining algorithms and models, with their diverse strengths and weaknesses, in optimisation applications (Han et al, 2020;Tóth et al, 2020;Ye et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

View full text Add to dashboard Cite

Artificial intelligence techniques are at the centre of a major shift in business today. They have a very broad array of applications within businesses, including that of optimisation for risk reduction in civil engineering projects. This is an active area of research, which has started to see real-world applications over the last few decades. It is still hindered by the extreme complexity of civil engineering problems and the computing power necessary to tackle these, but the economic and other benefits of these emerging technologies are too important to ignore. With that in mind, this chapter reviews the current state of research and real-world practice of optimisation techniques and artificial intelligence in risk reduction in this field. It also examines related promising techniques and their future potential.

show abstract

“… Accuracy improvement degree Han et al. ( 2020 ) kNN, SVM, RF Chiller system 2020 Assess using ensemble learning for chiller fault diagnosis. F1-score, confusion matrix Choi and Yoon ( 2021 ) NN-AE BAMS 2021 Investigate variants of the AE-based fault diagnosis approach for building automation systems.…”

Section: Overview Of Ai-big Data Analytic Frameworkmentioning

confidence: 99%

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

et al. 2022

View full text Add to dashboard Cite

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

show abstract

Ensemble learning with member optimization for fault diagnosis of a building energy system

Cited by 62 publications

References 47 publications

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

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

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

Contact Info

Product

Resources

About