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Processes
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Data-Driven Method for HVAC and Heat Pump System: From Monitoring...
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Data-Driven Method for HVAC and Heat Pump System: From Monitoring to Fault Detection and Diagnosis

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 11076

Special Issue Editors

Dr. Iole Nardi

E-Mail Website
Guest Editor
DUEE Dept., ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development), Casaccia Research Center, 00123 Rome, Italy
Interests: building physics; energy audit; HVAC; heat pump monitoring; building envelope; buildings energy performance; infrared thermography
Special Issues, Collections and Topics in MDPI journals
Dr. Domenico Palladino

E-Mail Website
Guest Editor
DUEE Department, ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development), Casaccia Research Center, 00123 Rome, Italy
Interests: building simulation; energy audit; building energy performance; CFD; artificial neural networks; thermal comfort; HVAC systems; energy analysis and forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To tackle climate change and achieve the ambitious targets set by energy policies, a great amount of effort is needed to reduce energy consumption and greenhouse gas emissions.

It is widely acknowledged that technical systems for heating, cooling and air-conditioning, and ventilation (HVAC) play an important role: on one hand, their efficiency strongly affects the final buildings’ consumption; on the other hand, they are primarily involved in the thermal comfort of users and occupants, who might overlook the energy and environmental effects of settings and control strategies.

In this framework, it is important to accurately assess the efficiency of HVAC systems, as well as their aging, fault income, or diagnosis, for the implications above and also for the economic drawbacks of maintenance.

Currently, the spread of affordable monitoring systems, sensing technologies, and advanced fault-detecting devices allows us to gather hundreds of empirical data for the purpose of fault detection and diagnosis, further aided by data-driven methods such as clustering methods, artificial intelligence (AI), big data, and the Internet of Things (IoT).

The goal of this issue is to bring researchers and stakeholders together to share their findings and present perspectives in the field of HVAC system monitoring, fault detection, and diagnosis based on data-driven methods.

Research papers, short communications, reviews, guidelines, project outcomes and lessons learnt are welcome on (but are not limited to) the following topics:

  • HVAC systems monitoring (efficiency assessment, fault detection, diagnosis, etc.);
  • Predictive maintenance and real-time condition monitoring systems;
  • Data-driven computing for HVAC systems;
  • Machine learning, AI, ANN, and big data for HVAC systems;
  • Measurements or simulations for assessing and enhancing HVAC system efficiency;
  • Changes in users’ awareness, attitudes, or habits after HVAC monitoring;
  • Computational methods of modelling faults;
  • Innovative sensing technology and devices for HVAC (including non-invasive techniques);
  • Advanced fault detection and diagnosis methods based on artificial intelligence (e.g., supervised/unsupervised machine learning).

Dr. Iole Nardi
Dr. Domenico Palladino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • HVAC monitoring
  • HVAC diagnosis
  • HVAC fault detection
  • HVAC aging effects
  • data-driven computing
  • ANN
  • AI
  • machine learning
  • random forest
  • sensing technologies and devices
  • NDT for HVAC
  • users’ awareness from real data
  • predictive maintenance

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Published Papers (3 papers)

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Research

21 pages, 3913 KiB  
Article
Performance Evaluation of Chiller Fault Detection and Diagnosis Using Only Field-Installed Sensors
by Zhanwei Wang, Jingjing Guo, Sai Zhou and Penghua Xia
Processes 2023, 11(12), 3299; https://doi.org/10.3390/pr11123299 - 26 Nov 2023
Cited by 2 | Viewed by 1877
Abstract
Owing to the rapid expansion of data science, data-driven methods have emerged as a dominant trend in chiller fault detection and diagnosis (FDD). Most of these methods prioritize feature selection to achieve optimal diagnostic performance. However, on-site research indicates a common installation of [...] Read more.
Owing to the rapid expansion of data science, data-driven methods have emerged as a dominant trend in chiller fault detection and diagnosis (FDD). Most of these methods prioritize feature selection to achieve optimal diagnostic performance. However, on-site research indicates a common installation of a limited number of sensors, coupled with a necessity to minimize diagnostic costs. This discrepancy between existing research’s feature selection principles and the current on-site sensor installation status presents a significant challenge. To facilitate the practical implementation of data-driven methods in real chiller units, this study addresses a critical question: under the constraint of limited on-site sensor installations, what is the optimal performance achievable by data-driven methods and their improved versions? To answer this, only features derived from commonly installed sensors on field chillers are chosen as indicators for typical chiller faults. The FDD performance of six frequently used data-driven methods, namely, back-propagation neural network, convolutional neural network, support vector machine, support vector data description, Bayesian network, and random forest, along with their improved versions, is comprehensively evaluated and validated using experimental data, considering four evaluation metrics. The conclusions drawn in this paper provide valuable insights for users/manufacturers with limited or no budget, detailing the best achievable diagnostic performance for each typical fault and offering guidance for those aiming to further enhance FDD performance. Full article
(This article belongs to the Special Issue Data-Driven Method for HVAC and Heat Pump System: From Monitoring to Fault Detection and Diagnosis)
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16 pages, 7906 KiB  
Article
Residential Buildings Heating and Cooling Systems: The Key Role of Monitoring Systems and Real-Time Analysis in the Detection of Failures and Management Strategy Optimization
by Giovanna Cavazzini and Alberto Benato
Processes 2023, 11(5), 1365; https://doi.org/10.3390/pr11051365 - 29 Apr 2023
Cited by 3 | Viewed by 1928
Abstract
Nineteen percent of global final energy consumption is used to generate electricity and heat in buildings. Therefore, it is undisputed that the building sector needs to cut consumption. However, this reduction needs to be driven by data analysis from real building operations. Starting [...] Read more.
Nineteen percent of global final energy consumption is used to generate electricity and heat in buildings. Therefore, it is undisputed that the building sector needs to cut consumption. However, this reduction needs to be driven by data analysis from real building operations. Starting from this concept and with the aim of proving the benefits deriving from the installation of a monitoring system in a real operating environment, in this work a monitoring system has been installed to monitor the centralised heating and cooling system of a residential building composed of 57 residential units. The data acquired from the installed sensors are collected and subsequently analysed in an ad hoc tool to detect anomalies, performance decay, malfunctions, and failures of the machines, as well as to understand if the implemented management strategy is appropriate in terms of energy and cost savings. The results show the key role of the data acquired by the monitoring system and analysed by the developed tool in terms of ability to detect failures and malfunctions in both the heating and cooling modes, as well as to help both in finding the proper management strategy and in identifying the performance deviation precursors of machine failure. Full article
(This article belongs to the Special Issue Data-Driven Method for HVAC and Heat Pump System: From Monitoring to Fault Detection and Diagnosis)
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26 pages, 4054 KiB  
Article
Development of Anomaly Detectors for HVAC Systems Using Machine Learning
by Davide Borda, Mattia Bergagio, Massimo Amerio, Marco Carlo Masoero, Romano Borchiellini and Davide Papurello
Processes 2023, 11(2), 535; https://doi.org/10.3390/pr11020535 - 10 Feb 2023
Cited by 16 | Viewed by 5662
Abstract
Faults and anomalous behavior affect the operation of Heating, Ventilation and Air Conditioning (HVAC) systems. This causes performance loss, energy waste, noncompliance with regulations and discomfort among occupants. To prevent damage, automated, fast identification of faults in HVAC systems is needed. Fault Detection [...] Read more.
Faults and anomalous behavior affect the operation of Heating, Ventilation and Air Conditioning (HVAC) systems. This causes performance loss, energy waste, noncompliance with regulations and discomfort among occupants. To prevent damage, automated, fast identification of faults in HVAC systems is needed. Fault Detection and Diagnosis (FDD) techniques are very effective for these purposes. The best FDD methods, in terms of cost effectiveness and data exploitation, are based on process history; i.e., on sensor data from automation systems. In this work, supervised and semi-supervised models were developed. Other than with regard to outdoor temperature and humidity, the input parameters of an HVAC system have few internal variables. Performance of traditional methods (e.g., VAR, Random Forest) is low, so Artificial Neural Networks (ANNs) were selected, since they can capture nonlinear relationships among features and are easily optimized. ANNs can detect simultaneous faults from different classes. ANN metrics are easily evaluated. The ground truth is obtained from process history (supervised case) and from a mix of deterministic methods and clustering (semi-supervised case). The derivation of the ground truth in the semi-supervised case, and extensive comparison with advanced supervised models, set this work apart from previous studies. The Mean Absolute Error (MAE) of the best supervised model was 0.032 over 15 min and 0.034 over 30 min. The Balanced Accuracy Score (BAS) of the best semi-supervised model was 86%. Full article
(This article belongs to the Special Issue Data-Driven Method for HVAC and Heat Pump System: From Monitoring to Fault Detection and Diagnosis)
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