scispace - formally typeset
Search or ask a question
Author

G. Menegazzo

Bio: G. Menegazzo is an academic researcher from University of Padua. The author has contributed to research in topics: HVAC & Fault detection and isolation. The author has an hindex of 1, co-authored 1 publications receiving 102 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: In this article, a semi-supervised data-driven approach is employed for fault detection and isolation that makes no use of a priori knowledge about abnormal phenomena for HVAC installations.

136 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: It is concluded that new artificial intelligence-based methodologies are needed to be able to combine the advantages of both kinds of methods in the future.
Abstract: Artificial intelligence has showed powerful capacity in detecting and diagnosing faults of building energy systems. This paper aims at making a comprehensive literature review of artificial intelligence-based fault detection and diagnosis (FDD) methods for building energy systems in the past twenty years from 1998 to 2018, summarizing the strengths and shortcomings of the existing artificial intelligence-based methods, and revealing the most important research tasks in the future. Challenges in developing FDD methods for building energy systems are discussed firstly. Then, a comprehensive literature review is made. All methods are classified into two categories, i.e. data driven-based and knowledge driven-based. The data driven-based methods are abundant, including the classification-based, unsupervised learning-based and regression-based. They showed powerful capacity in learning patterns from training data. But, they need a large amount of training data, and have problems in reliability and robustness. The knowledge driven-based methods show powerful capacity in simulating the diagnostic thinking of experts. But, they rely on expert knowledge heavily. It is concluded that new artificial intelligence-based methodologies are needed to be able to combine the advantages of both kinds of methods in the future.

280 citations

Journal ArticleDOI
TL;DR: A background on the challenges which may be encountered when applying anomaly detection techniques to IoT data is provided, with examples of applications for the IoT anomaly detection taken from the literature.
Abstract: Anomaly detection is a problem with applications for a wide variety of domains; it involves the identification of novel or unexpected observations or sequences within the data being captured. The majority of current anomaly detection methods are highly specific to the individual use case, requiring expert knowledge of the method as well as the situation to which it is being applied. The Internet of Things (IoT) as a rapidly expanding field offers many opportunities for this type of data analysis to be implemented, however, due to the nature of the IoT, this may be difficult. This review provides a background on the challenges which may be encountered when applying anomaly detection techniques to IoT data, with examples of applications for the IoT anomaly detection taken from the literature. We discuss a range of approaches that have been developed across a variety of domains, not limited to IoT due to the relative novelty of this application. Finally, we summarize the current challenges being faced in the anomaly detection domain with a view to identifying potential research opportunities for the future.

271 citations

Journal ArticleDOI
TL;DR: This study systematically surveyed how machine learning has been applied at different stages of building life cycle and can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings.

160 citations

Journal ArticleDOI
TL;DR: The outcome of this review shows that data-driven based approaches are more promising for the FDD process of large-scale HVAC systems than model-based and knowledge-based ones.

156 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed two representative smoothing techniques, which are based on a generic fault detection index in multivariate statistical process monitoring (MSPM), to detect incipient faults.

124 citations