Heating, Ventilation And Air Conditioning Units Research Articles

Impact of low-frequency noise on a residential zone due to commercial HVAC systems

Low frequency noise (LFN) is a serious growing global challenge for the urban environment where noise itself is an undervalued pollutant. The commercial establishments are equipped with multiple HVAC (Heating Ventilation and Air Conditioning) units responsible for the generation of LFN. To assess the impact of LFN, the noise was monitored using A and C-weighting sound level meters at HVAC outlets as well as in and around the residential premises. The noise due to HVAC units is found to be highly intensive in the lower frequency zones (<250Hz), affecting psychological and physiological health of living beings in the residential zones. A substantial 39.4 dB sound pressure level discrepancy between A and C-weighting inside the residential premises underscores the pronounced predominance of low-frequency noise. Noise monitoring using A-weighting failed to highlight the significant impact of lower frequency octave bands. The C-weighting noise monitoring revealed a substantial contribution of lower frequency octave bands (8Hz-250Hz) with 48.2 dB(C), in the overall equivalent noise level of 48.6(C), primarily attributed to HVAC units. The study suggests monitoring and assessment of the LFN, its mitigation and control strategies and its impact on the noise environment, especially on a residential zone.

Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach

With global warming intensifying and resource conflicts escalating, the world is undergoing a transformative shift toward sustainable practices and energy-efficient solutions. With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). Within a BEMSplatform, regulating the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is more important, noting that HVAC systems account for about 40% of the total energy cost in the commercial sector.This paper offers a Deep Reinforcement Learning (DRL) algorithm as a data-driven approach to controlling HVAC operation to enhance the energy efficiency of commercial buildings with open offices while ensuring thermal comfort for occupants in different zones. Compared to alternative methods such as rule-based models and model-predictive control, data-driven models have shown promising results in optimizing building energy consumption without the need for building-specific thresholds, prior knowledge about the underlying physics of heat distribution, and digital mapping of the airflow. Despite the astonishing performance of modern DRL methods in controlling energy management, a particular energy-saving solution for open-plan offices with multiple Variable Air Volume (VAV) systems, where different zones cannot be treated independently, is still missing. Also, some of the existing methods suffer from key issues such as long training time and lack of generalizability for using over-complicated models, incorporating external factors that are hard to model and characterize, and including factors that are not typically accessible. To solve these issues, we propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices, which uses only a handful of controllable and accessible factors. The efficacy of our solution is evaluated through extensive analysis of the overall energy consumption and thermal comfort levels compared to a baseline system based on the existing HVAC schedule from a real case. This comparison shows that our method achieves 37% savings in energy consumption with minimum temperature violation (<1%) of the desired temperature range during work hours. It takes only a total of 40 min for 5 epochs (about 7.75 min per epoch) to train a network with superior performance and covering diverse conditions for its low-complexity architecture; therefore, it easily adapts to changes in the building setups, weather conditions, occupancy rate, etc. Moreover, by enforcing smoothness on the control strategy, we suppress the frequent and unpleasant on/off transitions on HVAC units to avoid occupant discomfort and potential damage to the system. The generalizability of our model is verified by applying it to different building models and under various weather conditions.

Utilizing different artificial intelligence techniques for efficient condition assessment of building components

Facility management maintains building service quality through cycles of condition assessments and rehabilitation. Building components, however, differ in their nature, service lives, deterioration patterns, and textual/visual inspection data. This complicates the condition assessment process and subsequent rehabilitation decisions. This paper proposes a smart condition assessment framework that uses different artificial intelligence (AI) techniques that suit the condition data analysis of different building components. The framework has been applied to a dataset of over 2000 maintenance requests for roof and heating, ventilation, and air conditioning (HVAC) systems across a 600-villa portfolio. To address their varying needs, convolutional neural networks were used on images of roof defects, while enhanced data mining was used on textual data of HVAC systems. Accordingly, work packages of deteriorated components were identified, and a 60-day schedule was developed to repair 203 HVAC units. This research shows how AI can assist facility management with respect to condition assessment, rehabilitation planning, and resource allocation.

The effect of air velocity and indoor ambient properties on the effective operating range of an air ionizer device

Air ionizers are devices that purify the indoor air by artificially generating negative air ions (NAIs) which are proven to improve air quality and well-being of humans. Hence, more ionizers are being incorporated into heating, ventilation and air conditioning (HVAC) applications nowadays. . Every ionizer has its effective range of operation, which is the area inside a room that can potentially be covered by significant concentration of NAIs compared to the general concentration of ions naturally present in the room. This range may change depending on the indoor temperature, humidity and fan speed of the HVAC unit as preferred by the room occupants. Present research intends to design an experimental setup that could measure negative air ion (NAI) concentration at varying distances from an ionizer under different values of the aforementioned parameters. The experiment was conducted inside an indoor psychrometric test room to allow controlling of ambient temperature and humidity. Air velocity through the ionizer was varied from 1.5 m/s to 4.5 m/s, whereas the range of temperature and relative humidity were set from 25°C to 31°C and 60% to 80% respectively. To evaluate the ionizer’s operating range, NAI concentration was measured at every preset longitudinal (0 cm to 75 cm), lateral (-25 cm to 25 cm) and elevation (-15 cm to 15 cm) distance relative to the ionizer for 6 minutes using an ion counter named “Air Ion Counter Model AIC2”. The results reveal that the ionizer’s effective operating range increased with velocity, with the furthest range recorded at 4.5 m/s. Whereas a negative correlation was found between the ionizer’s effective operating range with temperature and humidity, with the furthest range recorded at 25°C and 60% respectively. The results also show that most ions were distributed in areas below and towards the right side from the ionizer’s perspective.

Reserve Evaluation and Energy Management of Micro-grids in Joint Electricity Markets Based on Non-Intrusive Load Monitoring

The heating, ventilation, and air-conditioning (HVAC) units are regarded as major demand response (DR) resources in micro-grids. However, due to privacy concerns and technical constraints, it is difficult for the system operator to obtain the complete information on each individual appliance. In this paper, we present a non-intrusive load monitoring (NILM) based framework for the operation strategy of the micro-grid in the joint energy, reserve, and regulation markets. The NILM technologies enable the operator to disaggregate the power of the HVAC units from the reading of the smart meters. Hence, the operation state of the appliances and the behavior of consumers can be studied without obtaining detailed data of each individual appliance. An advanced NILM algorithm is proposed, and the Coupled Generative Adversarial Networks (C-GAN) are utilized to enhance the generalizability of the trained model. Based on the NILM result, a novel method based on Hidden Markov Model (HMM) is proposed to evaluate the upward and downward reserve capacity of the HVAC units. The evaluated reserve capacity can help the operator better bid in the joint market based on the proposed optimization model. The proposed framework and methodology are verified through case studies. The simulation result reveals that with NILM, the market operator can save more energy consumption costs and load curtailment costs and earn more revenues in the joint market by selling excessive energy and providing ancillary services.

Chiller compressor sound emission analysis

Building occupants rely on heating, ventilation, and air conditioning (HVAC) units for comfort. Chillers are HVAC units that serve the purpose of cooling a building. Because chillers are often placed on top of or near the building, the noise from the chiller can adversely affect the occupants. A crucial component of all chillers, the compressor, is responsible for the majority of the noise produced by the chiller. In addition to designing for cooling efficiency, engineers must also consider how quiet and acoustically efficient a chiller is. In this project, we aim to develop an enclosure to house the compressor, therefore, reducing the magnitude of noise that is emitted. We begin by studying an empty cavity model in an acoustical software and observing its modes, resonant frequencies, and corresponding pressure distributions. Next, we placed a sound source within the cavity and then analyzed the acoustics frequency response of the cavity without and with a sound-absorbent material. We then proceeded to analyze a FEM model of the compressor and its dynamic frequency response due to the bearing force. After further analysis of the compressor sound emission, simulations will be made for the compressor with the designed enclosure and sound-absorbent material.

Energy Management Model for HVAC Control Supported by Reinforcement Learning

Heating, ventilating, and air conditioning (HVAC) units account for a significant consumption share in buildings, namely office buildings. Therefore, this paper addresses the possibility of having an intelligent and more cost-effective solution for the management of HVAC units in office buildings. The method applied in this paper divides the addressed problem into three steps: (i) the continuous acquisition of data provided by an open-source building energy management systems, (ii) the proposed learning and predictive model able to predict if users will be working in a given location, and (iii) the proposed decision model to manage the HVAC units according to the prediction of users, current environmental context, and current energy prices. The results show that the proposed predictive model was able to achieve a 93.8% accuracy and that the proposed decision tree enabled the maintenance of users’ comfort. The results demonstrate that the proposed solution is able to run in real-time in a real office building, making it a possible solution for smart buildings.

Preliminary symptoms assessment of typical faults related to the fans and humidifiers of HVAC systems based on experimental data collected during Italian summer and winter

The symptoms associated to the occurrence of typical faults in a heating, ventilation and air-conditioning (HVAC) system, including a single duct dual fan constant air volume air-handling unit, have been experimentally characterized. The operation of the HVAC unit with 3 artificially forced faults ((1) reduced velocity of the supply air fan, (2) reduced velocity of the return air fan, (3) the valve supplying the humidifier kept always closed) has been analysed and compared with that of healthy operation of the same plant under very similar boundary conditions (outside air temperature and initial indoor air temperature) during Italian summer and winter in order to preliminarily assess (i) the effects on the main operating parameters, and (ii) generate preliminary operation data to assist further research in fault detection and diagnosis of HVAC systems.

Acoustic Sensing and Noise Identification of a Heating, Ventilation and Air Conditioning Unit: Industrial Case Study

Reducing the noise and improving the sound quality of vehicles’ interior space is one of the challenges to enhance passengers’ experience. This is an ever-growing issue as entirely electric cars are becoming commonplace, making previously unnoticed noise a significant problem. Heating, Ventilation and Air Conditioning (HVAC) units are a major noise source in a vehicle’s interior space, yet automotive manufacturers only give a maximum dB specification to HVAC unit manufactures. Problematic noise is only typically identified once the unit is within the vehicle at the late stages of a project. Psychoacoustics is the study of human perception to sound, allowing unpleasant noise to be identified within recorded data. Within this study, an industrial prototype HVAC unit was analysed using a 96-channel acoustic camera capable of isolating and locating noise sources from the unit using beamforming. In addition to identifying the location of noise sources, several psychoacoustic metrics were used, such as sharpness and loudness, to identify undesirable noise within an extensive data set due to the vast range of test configurations. Testing was conducted to analyse the unit. Within the initial testing, an ‘annoying’ sound was identified at a particular motor RPM, and this was located using the camera to an area which indicated that it was a result of structural resonance. In addition, present was a high-frequency source which could not be located accurately. The results of this testing enable modifications to the unit to be made early in its’ development, either structurally to alter the resonance of the unit or within the settings to ensure certain RPMs are avoided.

Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment

The heating, ventilation, and air conditioning (HVAC) system serving the test room of the SENS i-Lab of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Aversa, south of Italy) has been experimentally investigated through a series of tests performed during both summer and winter under both normal and faulty scenarios. In particular, five distinct typical faults have been artificially implemented in the HVAC system and analyzed during transient and steady-state operation. An optimal artificial neural network-based system model has been created in the MATLAB platform and verified by contrasting the experimental data with the predictions of twenty-two different neural network architectures. The selected artificial neural network architecture has been coupled with a dynamic simulation model developed by using the TRaNsient SYStems (TRNSYS) software platform with the main aims of (i) making available an experimental dataset characterized by labeled normal and faulty data covering a wide range of operating and climatic conditions; (ii) providing an accurate simulation tool able to generate operation data for assisting further research in fault detection and diagnosis of HVAC units; and (iii) evaluating the impact of selected faults on occupant indoor thermo-hygrometric comfort, temporal trends of key operating system parameters, and electric energy consumptions.

Distributed and localized cooling with thermoelectrics

G. Jeffrey Snyder is a Professor of Materials Science and Engineering at Northwestern University in Evanston Illinois. His interests are focused on engineering of electronic and thermal properties, and he is well known for his work on thermoelectric materials. He has developed new methods of electron band structure engineering and microstructure engineering of thermal and electrical properties of complex materials. His interdisciplinary approach stems from his background in solid state chemistry at Cornell University and the Max Planck Institute for solid state research, applied physics at Stanford University, and thermoelectric materials and device engineering at NASA/Jet Propulsion Laboratory and California Institute of Technology (Caltech).

Versatile AHU fault detection – Design, field validation and practical application

This paper describes a new tool developed for the detection of operating faults in ventilation units with heat recovery. In principle, the tool is based on the APAR (Air Handling Unit Performance Assessment Rules) method. By following the semantic data description in accordance with the BrickSchema and Project Haystack initiatives, the tool is portable. The executive part of the fault detection system consists of several dozen detection rules, which simultaneously seeks to estimate wasted energy, the threat to user comfort, or the risk of reduced device lifespan, so that the detected faults can be sorted according to their severity.The developed detection tool was validated on real devices incorporated in a pilot plant. For validation purposes, the method of fault induction on real HVAC (Heating, Ventilation and Air Conditioning system) units was used, with subsequent inspection of whether the faults were revealed or not. The results revealed a 90% detection rate. The data set created as a result of this pilot plant is published as an annex to this article.In addition, the ability of the detection tool to reveal faults was also verified on the basis of data sets of measurements taken during the standard operation of several dozen HVAC units. The elimination of the identified operating faults generated energy savings of several thousands of dollars per year.

Experimental Calibration and Validation of a Simulation Model for Fault Detection of HVAC Systems and Application to a Case Study

Automated fault detection and diagnostics (FDD) could provide a cornerstone for predictive maintenance of heating, ventilation and air-conditioning (HVAC) systems based on the development of simulation models able to accurately compare the faulty operation with respect to nominal conditions. In this paper, several experiments have been carried out for assessing the performance of the HVAC unit (nominal cooling/heating capacity of 5.0/5.0 kW) controlling the thermo-hygrometric comfort inside a 4.0 × 4.0 × 3.6 m test room at the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Italy); then, a detailed dynamic simulation model has been developed and validated by contrasting the predictions with the measured data. The model has also been used to analyze the dynamic variations of key parameters associated to faulty operation in comparison to normal performance, in order to identify simplified rules for detection of any non-optimal states of HVAC devices. Finally, the simulated performance of the HVAC unit has also been investigated while serving a typical Italian building office with and without the occurrence of typical faults with the main aim of assessing the impact of the faults on thermo-hygrometric comfort conditions as well as electric energy consumption.

Investigation of the effects of an ultraviolet germicidal irradiation system on concentrations of aerosolized surrogates for common veterinary pathogens.

To determine whether exposure to UV germicidal irradiation (UVGI) reduces concentrations of viable aerosolized microorganisms (attenuated strains of common veterinary pathogens) in a simulated heating, ventilation, and air conditioning (HVAC) system. 42 air samples seeded with bacteriophage MS2 or attenuated strains of Bordetella bronchiseptica, feline calicivirus, feline herpesvirus-1, canine parvovirus, or canine distemper virus (6/microorganism) or with no microorganisms added (6). A simulated HVAC unit was built that included a nebulizer to aerosolize microorganisms suspended in phosphate-buffered water, a fan to produce airflow, 2 UVGI bulb systems, and an impinger for air sampling. Ten-minute trials (3 with UVGI, 3 without UVGI, and 1 negative control) were conducted for each microorganism. Impingers collected microorganisms into phosphate-buffered water for subsequent quantification with culture-based assays. Results for samples yielding no target microorganisms were recorded as the assay's lower limit of detection. Statistical analysis was not performed. The UVGI treatment resulted in subjectively lower concentrations of viable MS2, B bronchiseptica, and canine distemper virus (arithmetic mean ± SD log10 microorganism reduction, 2.57 ± 0.47, ≥ 3.45 ± 0.24, and ≥ 1.50 ± 0.25, respectively) collected from air. Feline herpesvirus-1 was detected in only 1 sample without and no samples with UVGI treatment. Feline calicivirus and canine parvovirus were not detectable in any collected samples. Results for some surrogates of veterinary pathogens suggested a potential benefit to supplementing manual disinfection practices with UVGI-based air cleaning systems in animal care environments. Further research is needed to investigate the utility of UVGI in operating HVAC systems.

Improving power quality efficient in demand response: Aggregated heating, ventilation and air-conditioning systems

This study aims to identify the role of aggregated heating, ventilation, and air conditioning (HVAC) loads based on system characteristics using the lazy state switching control mode focusing on the overall power consumption rather individual response speed. This study is attempted to provide secondary frequency regulation using aggregated HVAC loads with more stable operation with the lazy state switching control mode based on conditional switching of the HVAC unit’s working state. The stability of power consumption improves power quality in smart grid design and operation. The aggregated HVAC must reach a stable condition before tracking the automatic generation control signal and fully developed smart grids complex structure. Still, HVAC slowed responses make inappropriate for faster demand response services. Unsuitable control algorithm leads to system instability and HVAC unit overuse. An extended command processing on the client side is proposed to deal with the adjusting command. The unique advantages of the proposed algorithm are three folds. (1) the control algorithm preserves its working state and has nothing conflicting with the lockout constraints for individual system units; (2) the control algorithm shows promising performance in smoothing the overall power consumption for the aggregated population; and (3) the control logic is fully compatible with other control algorithms. The proposed modeling and control strategy are validated against simulations of thousands of units, and the simulation result indicates that the proposed approach has promising performance in smoothing the power consumption of aggregate units’ population.

Determination of consumer behavior based energy wastage using IoT and machine learning

We develop a low-cost, non-intrusive methodology for the determination of consumer-behavior-based-energy-wastage (CBB-EW) in the operation and control of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Using data from temperature and humidity sensors, we develop Machine Learning (ML) models of heat flow from the environment (environment-component) and heat flow due to HVAC operation (HVAC-component). Environment-component and HVAC-component models are used to determine HVAC ON/OFF status and HVAC energy consumption. We divide CBB-EW into two components, i.e., non-occupancy-based wastage (CBB-EW-NOC) and occupancy-based wastage (CBB-EW-OCC). With the help of HVAC status, motion sensor, and contextual information, we propose a data fusion approach to determine CBB-EW-NOC. We use predicted mean voting (PMV) model to determine the optimal thermal settings required to operate HVAC unit in the neutral PMV range. The difference between the amount of energy consumed by the HVAC unit at the optimal and user-controlled thermal settings allows us to compute CBB-EW-OCC. We validate our proposed methodology using actual data from an experimental case study comprising of users controlling HVAC units in their separate office rooms. We observe that users generally waste a large amount of energy (more than 50% in some cases) due to unnecessary HVAC operation and sub-optimal thermal settings. Furthermore, energy wastage pattern of different users also differ significantly, which implies the requirement of customized feedback and interventions to inculcate energy-conservation behavior.

A Supervised-Learning-Based Strategy for Optimal Demand Response of an HVAC System in a Multi-Zone Office Building

The thermal capacity of buildings enables heating, ventilating, and air-conditioning (HVAC) systems to be exploited as demand response (DR) resources. Optimal DR of HVAC units is challenging, particularly for multi-zone buildings, because this requires detailed physics-based models of zonal temperature variations for HVAC system operation and building thermal conditions. Using supervised learning (SL), this paper proposes a new strategy for optimal DR of an HVAC system in a multi-zone building. Artificial neural networks (ANNs) are trained with data obtained under normal building operating conditions. The ANNs are replicated using piecewise linear equations, which are explicitly integrated into an optimization problem for price-based DR. The problem is solved for various electricity prices and building thermal conditions. The solutions are used to train a deep neural network (DNN) to directly determine the optimal DR schedule, which is termed as SL-aided meta-prediction (SLAMP) here; the DNN can work as a price-and-optimal-demand curve. Case studies are performed using three different methods: explicit ANN replication, SLAMP, and physics-based modeling. The case study results verify that the proposed SL-based strategy is effective in terms of both practical applicability and computational time, while also ensuring the thermal comfort of occupants and cost-effective operation of the HVAC system.

Managing thermal comfort in contemporary high-rise residential buildings: Using smart thermostats and surveys to identify energy efficiency and comfort opportunities

Heating, ventilation and air conditioning (HVAC) operation is the largest contributor to residential building energy use and can significantly influence occupant thermal comfort and behaviours. Despite the boom in high-rise residential building construction, little is known about the relationship between occupant comfort and behaviors and HVAC operation in these contemporary buildings. In this study, connected thermostat data and occupant surveys from 55 participants across two contemporary high-rise condominium buildings were used to characterize this relationship to reveal opportunities for improved comfort and energy efficiency. Survey data indicated that occupant thermal discomfort was prevalent across both buildings in the heating and cooling seasons (48% and 53% reporting discomfort in heating and cooling seasons, respectively). The measured data corroborated the survey findings showing that over-conditioning of suites is a chronic issue across seasons. However, investigation into the temperatures of suites located in different parts of the buildings indicated that the overheating was influenced by wind, solar radiation and winter stack effect, but not in the ways suggested by building physics (e.g. air moving in opposite direction of predicted stack effect). This supports our hypothesis that the MAU operational characteristics and incorrect balancing of air duct networks are likely causing over-conditioning but further investigation is ongoing to confirm this. Analysis of suite HVAC unit runtimes also revealed low runtimes in heating season, which may limit the effectiveness of improved (e.g. occupant-based) in-suite HVAC controls, given the minimal space conditioning energy consumption in these particular suites. This study demonstrates the potential for using connected thermostat data as a diagnostic tool to identify opportunities for energy savings in the building.

A Review of Fault Detection and Diagnosis Methodologies for Air-Handling Units

HVAC (Heating, Ventilation and Air-Conditioning) systems for space heating, space cooling and ventilation of buildings consume nearly 40% of the world energy demand and present the least expensive opportunities for reducing the greenhouse gases emission. Fault Detection and Diagnosis (FDD) methods could monitor the operation of various processes and/or components allowing to detect and, if possible, even predict the presence of defects (deviations from normal or expected operation) as well as ideally identify (diagnose) the fault and/or its location, giving instructions for undertaking corrective actions. FDD techniques could be successfully used for managing the predictive maintenance and/or optimizing the energy/economic/environmental performance of HVAC units while assuring the comfort of occupants. This paper examines the current state of the art of the research on the development and implementation of FDD systems when applied to Air-Handling Units (AHUs), the main and most important device of HVAC systems. This paper describes the existing methodologies, approaches and tools for the utilization of FDD techniques, summarizes the most important findings available in current literature in reference to several case studies where FDD systems have been applied with reference to AHUs and indicates the main gaps to be further investigated.

Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.