Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review

Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over the last few decades. NILM can play an important role in providing future energy efficiency feedback and developing fault detection and diagnosis (FDD) tools in smart buildings. Therefore, the review of NILM-based methods for FDD and the energy efficiency (EE) assessment of HVACs can be beneficial for users as well as buildings and facilities operators. To the best of the authors’ knowledge, this paper is the first review paper on the application of NILM techniques in these areas and highlights their effectiveness and limitations. This review shows that even though NILM could be successfully implemented for FDD and the EE evaluation of HVACs, and enhance the performance of these techniques, there are many research opportunities to improve or develop NILM-based FDD methods to deal with real-world challenges. These challenges and future research works are also discussed in-depth.

Measurements for non-intrusive load monitoring through machine learning approaches

The topic of non-intrusive load monitoring (NILM) has seen a significant increase in research interest over the past decade, which has led to a significant increase in the performance of these systems. Nowadays, NILM systems are used in numerous applications, in particular by energy companies that provide users with an advanced management service of different consumption. These systems are mainly based on artificial intelligence algorithms that allow the disaggregation of energy by processing the absorbed power signal over more or less long time intervals (generally from fractions of an hour up to 24 h). Less attention was paid to the search for solutions that allow non-intrusive monitoring of the load in (almost) real time, that is, systems that make it possible to determine the variations in loads in extremely short times (seconds or fractions of a second). This paper proposes possible approaches for non-intrusive load monitoring systems operating in real time, analysing them from the point of view of measurement. The measurement and post-processing techniques used are illustrated and the results discussed. In addition, the work discusses the use of the results obtained to train machine learning algorithms that allow you to convert the measurement results into useful information for the user.

Quantifying Training and Game Demands of a National Basketball Association Season

Purpose: There are currently no data describing combined practice and game load demands throughout a National Basketball Association (NBA) season. The primary objective of this study was to integrate external load data garnered from all on-court activity throughout an NBA season, according to different activity and player characteristics.Methods: Data from 14 professional male basketball players (mean ± SD; age, 27.3 ± 4.8 years; height, 201.0 ± 7.2 cm; body mass, 104.9 ± 10.6 kg) playing for the same club during the 2017–2018 NBA season were retrospectively analyzed. Game and training data were integrated to create a consolidated external load measure, which was termed integrated load. Players were categorized by years of NBA experience (1-2y, 3-5y, 6-9y, and 10 + y), position (frontcourt and backcourt), and playing rotation status (starter, rotation, and bench).Results: Total weekly duration was significantly different (p < 0.001) between years of NBA playing experience, with duration highest in 3–5 year players, compared with 6–9 (d = 0.46) and 10+ (d = 0.78) year players. Starters experienced the highest integrated load, compared with bench (d = 0.77) players. There were no significant differences in integrated load or duration between positions.Conclusion: This is the first study to describe the seasonal training loads of NBA players for an entire season and shows that a most training load is accumulated in non-game activities. This study highlights the need for integrated and unobtrusive training load monitoring, with engagement of all stakeholders to develop well-informed individualized training prescription to optimize preparation of NBA players.

Diagnostic utility of saliva and its implication in detection of Covid-19 and other diseases

Saliva is a valuable tool for early detection, better treatment, and a better prognosis. Early detection of illnesses is sometimes challenging, and it necessitates additional clinical and laboratory tests, which can delay treatment and have a significant impact on prognosis. A large range of chemicals may be found in saliva, providing useful information for clinical diagnostic purposes.The coronavirus disease pandemic (Covid-19) is the world's largest challenge and global health disaster since World War II. Controlling the epidemic in the community and in hospitals requires a quick and precise diagnosis of Covid-19. For Covid-19 diagnostic testing, nasopharyngeal and oropharyngeal swabs are the suggested specimen types.The collection of these specimens necessitates intimate contact between healthcare staff and patients, which increases the risk of viral transmission. As a result, nasopharyngeal or oropharyngeal swabs are not recommended for sequential viral load monitoring. Saliva specimens are simply collected by having the patient spit into a sterile container. Saliva collection is non-invasive and significantly reduces healthcare personnel' exposure to Covid-19. To develop quick chair side assays for the detection of Covid-19, more study is needed to investigate the potential diagnostic of Covid-19 in saliva.

External and internal load during training sessions in elite women’s soccer: a systematic mini review

Review question / Objective: The present systematic mini review aim to provide an overview about external and internal load during training sessions in elite women’s soccer, with special focus on fatigue, training adaptions and injuries. Condition being studied: Continuous training load monitoring in the context of the regular team routine. Eligibility criteria: To investigate continuous monitoring, we include articles with a minimum of one week of monitoring, irrespective of gender and study focus (e.g. studies reporting descriptive data of training load without studying its effects will be included). Articles will be excluded if: the participants are not all elite women’s soccer players (e.g. mixed samples including elite and non-elite players); the participants are aged under 18; the participants are not monitored longitudinally over a minimum of a 1-week period or five sessions (if the duration is not stated; friendly matches are considered training sessions) to consider continuous monitoring practices; no GPS-derived training load data are reported; the articles do not report any training load indicators; single drills are monitored rather than the entire training session, or the article focuses on the comparison between a specific drill and match demands; data from training sessions are not reported; and the articles are editorials or reviews.

Machine Learning Based Approach to a Crane Load Estimation

In the presence of increasing demands for safety and efficiency of material handling systems, the development of advanced supervisory control, monitoring, data acquisition and diagnostic systems is involved, especially for large industrial cranes. The important part of such systems is the continuous monitoring of a crane load. The crane load monitoring system proposed in the paper is based on a fuzzy model that estimates a payload mass transferred by a crane based on measuring the crane girder deflection and trolley position. The model was identified using the fuzzy subtractive clustering and least mean square with the data collected during experiments carried out on the laboratory scaled overhead crane.

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach

Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household’s power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements.