Review of predictive maintenance algorithms applied to HVAC systems

In this work, HVAC (Heating, Ventilation and Air Conditioning) systems applied in university buildings with control based on PMV (Predicted Mean Vote) and aPMV (adaptive Predicted Mean Vote) indexes are discussed. The building’s thermal behavior with complex topology, in transient thermal conditions, for summer and winter conditions is simulated by software. The university building is divided into 124 spaces, on two levels with an area of 5931 m2, and is composed of 201 transparent surfaces and 1740 opaque surfaces. There are 86 compartments equipped with HVAC systems. The simulation considers the actual occupation and ventilation cycles, the external environmental variables, the internal HVAC system and the occupants’ and building’s characteristics. In this work, a new HVAC control system, designed to simultaneously obtain better occupants’ thermal comfort levels according to category C of ISO 7730 with less energy consumption, is presented. This new HVAC system with aPMV index control is numerically implemented, and its performance is compared with the performance of the same HVAC system with the usual PMV index control. Both HVAC control systems turn on only when the PMV index or the aPMV index reaches values below −0.7, in winter conditions, and when the PMV index or the aPMV index reaches values above +0.7, in summer conditions. In accordance with the results obtained, the HVAC system guarantees negative PMV and aPMV indexes in winter conditions and positive PMV and aPMV indexes in summer conditions. The energy consumption level is higher in winter conditions than in summer conditions for compartments with shading, and it is lower in winter conditions than in summer conditions for compartments exposed to direct solar radiation. The consumption level is higher using the PMV control than with the aPMV control. Air temperature, in accordance with Portuguese standards, is higher than 20 °C in winter conditions and lower than 27 °C in summer conditions. In Mediterranean climates, the HVAC systems with aPMV control provide better occupants’ thermal comfort levels and less energy consumption than the HVAC system with PMV control.

Almost every building in the United States has a HVAC (Heating, ventilation, and air conditioning) system that relies on balancing Velocity Pressure and calculated CFM to properly control the atmosphere in a building. This is ensured and tested by Field Engineers whose job it is to maintain the efficiency and proper output of these HVAC systems. These systems are used to improve indoor air quality, control moisture and condensation, and contaminants while satisfying comfort needs of occupants. Just with any mechanical system, HVAC systems function efficiently with multiple formulas derived from calculus and physics. Every building is equipped with a system that needs to meet a certain threshold of air movement, measured in Cubic Feet per Minute (CFM), that creates a stable and comfortable indoor atmosphere by testing and balancing these systems with Velocity Pressure. Velocity Pressure is the base reading it takes to understand and adjust air movement within a HVAC system to guarantee the overall best quality of an indoor air environment. Once a measurement of Velocity Pressure, measured in inches of water column (in-WC) using a tool called a Flow Hood or Straight Pitot Tube, is taken then you can start to calculate flow readings and test if a fan is producing the correct amount of CFM that the system was built to produce and support. This testing is done by using a series of calculus and algebraic equations that takes area and velocity into consideration. When solving these formulas, it can then be determined if a HVAC system and its fans are producing the ideal amount of airflow and air quality to accurately produce the precise environment for any building.

Efficient management of heating, ventilation, and air conditioning (HVAC) systems is paramount for mitigating energy consumption and enhancing climate resilience in modern infrastructure. This review explores the significance of leveraging project management tools to optimize energy efficiency within HVAC operations, thus fostering a pathway towards climate resilience. The integration of project management methodologies offers a systematic approach to address challenges associated with energy consumption in HVAC systems, thereby facilitating informed decision-making processes. Firstly, the review delves into the pressing need for energy conservation in HVAC operations amidst escalating concerns over climate change and its adverse impacts. The imperative to minimize energy consumption in buildings, particularly through HVAC systems, is highlighted as a pivotal step towards achieving climate resilience and sustainability goals. Subsequently, the review outlines the role of project management tools in orchestrating effective strategies to enhance energy efficiency within HVAC operations. By employing techniques such as project scheduling, risk management, and resource allocation, project managers can streamline the implementation of energy-efficient measures, thereby optimizing HVAC system performance and reducing carbon emissions. Moreover, the review underscores the importance of data analytics and technological advancements in augmenting the efficacy of project management tools for energy efficiency in HVAC operations. Leveraging real-time data monitoring, predictive analytics, and IoT-enabled devices enables proactive maintenance and continuous optimization of HVAC systems, thereby maximizing energy savings and bolstering climate resilience. Furthermore, the review elucidates the potential benefits and challenges associated with the adoption of project management tools for energy efficiency in HVAC operations. While improved cost-effectiveness, environmental sustainability, and operational performance emerge as primary benefits, challenges such as initial investment costs, technological complexities, and organizational inertia necessitate careful consideration. This review advocates for the integration of project management tools as a viable approach to foster energy efficiency in HVAC operations, thereby paving the way towards enhanced climate resilience and sustainability in built environments. By embracing innovative methodologies and technological solutions, stakeholders can mitigate energy consumption, reduce greenhouse gas emissions, and fortify infrastructure against the impacts of climate change. Keywords: HVAC, Climate Resilience, Project Management, Energy, Review.

Increasing interest has been expressed towards intelligent heating, ventilation and air conditioning (HVAC) systems in hospital environments. This article presents a literature review of intelligent HVAC systems used in hospitals. First, we discuss the current state of HVAC systems and common ventilation issues regarding modern hospitals. Second, we describe hospital airflow modelling using computational fluid dynamics. We then define ‘intelligent HVAC systems’ and address challenges concerning their design and implementation. Lastly, possibilities for HVAC system optimization and energy conservation are presented.

The main objective of this on-site study is to use a full-scale HVAC (Heating, Ventilating and Air-Conditioning) system installed in an office building in Taiwan for comparing the power consumption, energy-saving, and CO2reduction by variable primary flow (VPF) chilled water systems for controlling the HVAC. The on-site experimental results indicate that average power consumptions are 44,935 kWh for the constant volume (CV) chilled water systems, and 39,025 kWh for the VPF system. Approximately 19% of the maximum energy-saving rate can be achieved if the HVAC system is operated as a VPF system. Additionally, the reduced quantity of accumulated CO2emission varies from 0 to 44,186 kg with 0.623 kg-CO2kwh-1emission factor during the office hours of 08:00 (a.m.) to 20:00 (p.m.). The results demonstrate that switching the operation of an office building HVAC system from CV to VPF will significantly enhance energy savings and CO2reduction. This study is will offer useful information for evaluating an indoor environmental policy with respect to energy savings and CO2emission reduction for office HVACs used in subtropical regions.

A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations

Energy-efficient fuzzy model-based multivariable predictive control of a HVAC system

Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems

This paper presents detailed anomaly detection evaluation on operational time-series data of Internet of Things (IoT) based household devices in general and Heating, Ventilation and Air Conditioning (HVAC) systems in specific. Due to the number of issues observed during evaluation of widely used distance-based, statistical-based, and cluster-based anomaly detection techniques, we also present a pattern-based approach for anomaly detection in HVAC time-series data. The usage and number of IoT based HVAC systems are enormously increasing and will have a major share in IoT based household devices in the near future. The operational and usage log of these devices contains different sensor values logged with time, containing normal data points, and long-term anomalies. The state-of-the-art methods for anomaly detection are unable to detect these long-term anomalies, which reflect the deteriorating effect of a sensor. The presented approach overcomes this problem by building a knowledge base of long/short -term patterns based on normal data points which keep growing over the time. In addition to the detected anomalies and in contrast to the existing methods, the presented method gives meaningful anomaly score for a number of HVAC systems. We evaluate the presented approach on real operational data, collected over the period of 2.5 years. Evaluation results show that the presented approach outperforms the state-of-the-art methods for anomaly detection with the area under the curve (AUC) value of 99.4%. Discord detection results of the proposed technique on another dataset from a different domain show the generic and adaptive nature of our technique.

The heating, ventilation, and air conditioning (HVAC) system is a major energy consumer in office buildings, and its operation is critical for indoor thermal comfort. While previous studies have indicated that reinforcement learning control can improve HVAC energy efficiency, they did not provide enough information about end-to-end control (i.e., from raw observations to ready-to-implement control signals) for centralized HVAC systems in multizone buildings due to the limitations of reinforcement learning methods or the test buildings being single zones with independent HVAC systems. This study developed a model-free end-to-end dynamic HVAC control method based on a recently proposed deep reinforcement learning framework to control the centralized HVAC system of a multizone office building. By using the deep neural network, the proposed control method could directly take measurable parameters, including weather and indoor environment conditions, as inputs and control indoor temperature setpoints at a supervisory level. In some test cases, the proposed control method could successfully learn a dynamic control policy to reduce HVAC energy consumption by 12.8% compared with the baseline case using conventional control methods, without compromising thermal comfort. However, an over-fitting problem was noted, indicating that future work should first focus on the generalization of deep reinforcement learning.

A review of different strategies for HVAC energy saving

Air quality is known to have significant health impacts from both short- and long-term exposure. As a significant contributor, the transport sector is working to reduce its adverse impact; however, the impact of rail transport on air quality at local levels, particularly within enclosed spaces (e.g. stations and onboard trains), is still not fully understood. With 30% of rail traction diesel-powered, previously studies have shown that in certain conditions pollution concentrations exceed prescribed EU limits in enclosed stations. Although these studies offer insight into issues at particular strategic locations, there have been none which address issues and identify mitigation measures to risks associated with poor air quality for both passengers and train crews onboard trains for typical journeys. This seminar will present work conducted as part of the Rail Safety and Standards Board (RSSB) Clean Air Research programme on project “T1234 Air Quality on Trains - HVAC and Exhaust Interactions Study”, which focused on providing better understanding of rail’s direct contribution to air pollution onboard trains. The project aimed to determine how exhaust emissions and heating, ventilation, and air conditioning (HVAC) systems interact and impact levels of air pollution onboard passenger trains, establishing factors that can be changed to improve air quality. The programme covered a wide range of techniques, from computational studies through to model- and full- experimental validation. Findings indicated that the design configuration of train roofs were particularly important when considering the interaction of exhaust gases with HVAC systems and that simple alterations in design and shape of the roof and exhaust pipe, as well as the HVAC placement and operation could lead to large improvements in onboard air quality

The heating, ventilation, and air conditioning (HVAC) system plays a crucial role in ensuring the safety of workers and preventing the release of gaseous radioactive materials into the environment during the decommissioning of a nuclear power plant (NPP). To establish an HVAC operation strategy, decommissioning phases were divided into four stages, and the HVAC systems were reclassified. In addition, assumptions have been made regarding design modifications and maintenance for the reactor containment building (RCB) HVAC, fuel handling building (FHB) HVAC, and main control room (MCR) HVAC. Based on these, for RCB HVAC operation, natural ventilation and RCB purge operation during the transition period are proposed. In the decommissioning stage, recirculation operation, entire ventilation operation consisting of continuous operation and purge operation, and finally partial ventilation operation to purify local space were proposed. Moreover, during the transition period, the FHB HVAC was proposed to operate as normal NPP, and the MCR HVAC was suggested to operate with safety-related equipment removed.

During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods.

Respiratory viruses are capable of transmitting via an aerosol route. Emerging evidence suggests that SARS-CoV-2 which causes COVID-19 can be spread through airborne transmission, particularly in indoor environments with poor ventilation. Heating, ventilation, and air conditioning (HVAC) systems can play a role in mitigating airborne virus transmission. Ultraviolet germicidal irradiation (UVGI), a feature that can be incorporated into HVAC systems, can be used to impede the ability of viruses to replicate and infect a host. We conducted a systematic review of the scientific literature examining the effectiveness of HVAC design features in reducing virus transmission—here we report results for ultraviolet (UV) radiation. We followed international standards for conducting systematic reviews and developed an a priori protocol. We conducted a comprehensive search to January 2021 of published and grey literature using Ovid MEDLINE, Compendex, and Web of Science Core. Two reviewers were involved in study selection, data extraction, and risk of bias assessments. We presented study characteristics and results in evidence tables, and synthesized results across studies narratively. We identified 32 relevant studies published between 1936 and 2020. Research demonstrates that: viruses and bacteriophages are inactivated by UV radiation; increasing UV dose is associated with decreasing survival fraction of viruses and bacteriophages; increasing relative humidity is associated with decreasing susceptibility to UV radiation; UV dose and corresponding survival fraction are affected by airflow pattern, air changes per hour, and UV device location; and UV radiation is associated with decreased transmission in both animal and human studies. While UV radiation has been shown to be effective in inactivating viruses and reducing disease transmission, practical implementation of UVGI in HVAC systems needs to consider airflow patterns, air changes per hour, and UV device location. The majority of the scientific literature is comprised of experimental, laboratory-based studies. Further, a variety of viruses have been examined; however, there are few studies of coronaviruses and none to date of SARS-CoV-2. Future field studies of UVGI systems could address an existing research gap and provide important information on system performance in real-world situations, particularly in the context of the current COVID-19 pandemic. This comprehensive synthesis of the scientific evidence examining the impact of UV radiation on virus transmission can be used to guide implementation of systems to mitigate airborne spread and identify priorities for future research.Trial registration PROSPERO 2020 CRD42020193968.