Heating, Ventilation, And Air Conditioning Equipment Research Articles

Detailed Analysis of Energy Consumption for an Office Building

Modern buildings consume a significant share of global power production. The primary sources of the power consumption are heating, ventilation and air conditioning (HVAC), light, cooking equipment, technical equipment like elevators, office facilities. Optimization of the buildings power consumption requires real-life data both for strategic planing and operational control. Here we analyze detailed power consumption of a research institute campus which is a medium-sized office buildings. Our data contain detailed electric measurements of 100 three-phase power lines which power all facilities inside the building. Each line is associated with several power consumers like lights, HVAC and office equipment. We analyze weekly and monthly trends, typical patterns in power consumption, and provide rough analysis of what classes of power consumers are most active in specific periods of time.

Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network

Modeling of the chiller performance is essential for the implementation of optimal energy-efficient control strategies in a heating, ventilation, and air conditioning (HVAC) system. Though classical data-driven chiller performance models are widely adopted in the industry, they generally suffer from poor out-of-sample generalization abilities, which refers to the model's capability to extrapolate for new data outside the range of the training dataset. In practice, however, the available chiller operation data for model development are often insufficient or collected from a few limited operating conditions, such that extrapolation is unavoidable after the model is applied for control purposes. To deal with this issue, this paper proposed a physics-guided neural network (PGNN) to model the energy performance of chillers. By adopting a new neural network architecture, modifying the loss function, and adding limited out-of-sample data, the PGNN incorporates domain knowledge into the data-driven model to achieve better out-of-sample generalization performance. Meanwhile, the convexity and monotonicity between the dependent and independent variables in the PGNN are properly addressed. The proposed PGNN is applied to model the chiller serving a high-rise building, and results show that PGNN performs much better in extrapolation than classical models and the multi-layer perceptron model. The research demonstrated the usefulness and effectiveness of the PGNN in modeling HVAC equipment.

Preliminary Research on Water Supply, Drainage, and HVAC Equipment in Shanghai Historic Buildings During the Northern Warlord Period

During the Northern Warlord Period (1912–1928), the construction industry in Shanghai underwent robust development. As an integral element of buildings, equipment served both functional purposes and stood witness to the evolution of the economy and society, thus earning its place as part of the architectural heritage. However, due to various reasons, there are many loopholes in the protection of these building equipment. This paper examines the development of building equipment in Shanghai during the Northern Warlord Period, using water supply, drainage, and heating, ventilation, and air conditioning (HVAC) systems as examples. Through historical context analysis, it summarizes this development from a social-spatial perspective, infers the reasons behind it, and analyzes the importance of preserving such equipment, considering both past and present viewpoints. In this research, the importance of protecting historical building equipment is emphasized, which aims to give people a deeper understanding of their cultural value, and suggests that scholars conduct more practical research on their protection.

Performance analysis of triple glazing water flow window systems during winter season

Water is circulated in the space between glazing as part of the Water Flow Window (WFW) technology. This idea may have an impact on the energy consumption of HVAC (heating, ventilation, and air conditioning) equipment. WFW is commonly used to remove excess heat; the excess heat can be stored. The purpose of this study is to compare the performance of triple glazing WFW under environmental conditions of Turkey during the winter. To compare the electrical energy consumption of the HVAC devices of testing cabinets, three identical testing cabinets with three different glazing types; standard (air-filled) window, WFW system with energy storage tank and domestic water supported WFW system are employed. The Thermal Energy Storage (TES) tank is a water tank that contains two distinct Phase Change Materials (PCM). PCMs are positioned all around and crammed inside water-filled pistol-style tubes. The TES tank volume is 5.675 L (42% water, 46% RT18 HC, 12% RT22 HC). The windows' surface area is approximately 0.3 m2. Visualization of the melting process in the TES tank is obtained with the numerical study. Results show that the consumption ratio of cooling devices can fall under 21% for daytime and heating devices can fall under 79% for nighttime.

Investigation of the impact of phase change materials at different building envelope placements in a semi-arid climate

Researchers in the building industry are becoming increasingly interested in phase change materials (PCM). Due to the potential for passive temperature management offered by these PCMs, HVAC (Heating, Ventilation, and Air-Conditioning) equipment will use less energy. Still, to prevent leakage during phase change, an enclosure is needed when installing PCMs in buildings. So, to determine this passive indoor temperature regulation cost, payback period, and energy savings, it is crucial to assess the PCM energy savings in various building envelope placements. So, this is done by using the EnergyPlus simulation engine for the case of the commercial PCM RT 28HC, this article will assess and compare 8 potential building envelope positions in a semi-arid region. These eight different building configurations have one case without PCM and the other cases have PCM placed in the interior surface of the following specific placements, a PCM placed in walls envelope only, in floors envelope, in ceilings and roofs envelope only, in walls, ceilings, and roofs envelope, in walls and floors envelope only, in floors, ceilings and roofs envelope and all envelopes for a 10 m square-shaped building. This study's primary goal is to evaluate the reduction of indoor air temperature fluctuations, PCM energy savings, PCM activation, and payback period for these various building layouts. The findings show excellent temperature indoor stabilization in summer, for the case with PCM installed on all interior envelope surfaces showing a maximum indoor temperature fluctuation decrease of 110.2 °C for August. In addition, the case with the PCM placed only in the walls, roof, and ceilings has shown the highest yearly energy savings, nevertheless, the case with PCM placed only in the walls has the lowest payback period of nearly 22 years and a half.

Performance benchmarking of residential air conditioning systems using smart thermostat data

Faulty residential Heating, Ventilation, and Air Conditioning (HVAC) systems waste billions of dollars of energy usage in the U.S. each year, meanwhile heavily affecting occupant comfort and causing complete operational failures. Fault detection and diagnosis (FDD) methods assist technicians in discovering and locating faults as early as possible, thereby optimizing system efficiency through predictive maintenance. However, the majority of FDD methods are developed for commercial HVAC systems. This is because FDD needs data acquired from sensors installed on the HVAC equipment, but in the residential sector, the initial cost of installing additional sensors is uneconomical. In recent years, the development of smart thermostats and the Internet of Things (IoT) enables large-scale system operational data to be recorded. The collection of smart thermostat data has nearly no cost, and FDD can be conducted using aggregated data obtained from a large number of systems using IoT technologies. Hence, this paper proposes a performance benchmarking method for residential systems that only use smart thermostats as the data source. The proposed method analyzes the system pseudo steady-state behavior and compares a critical feature termed the weighted average difference of cooling effort (ΔE¯c) both between systems and within each system. Three applications of the proposed method are detailed: identification of inadequate capacity, quantification of active capacity degradation, and verification of repair after maintenance. Within each application, real faulty systems are illustrated to demonstrate the usefulness of this method.

Effect of Window Openable Area and Shading on Indoor Thermal Comfort and Energy Efficiency in Residential Buildings with Various Operating Modes

Solar heat gain and natural ventilation cooling of the indoor environment in buildings are highly influenced by the shading and openable area of windows. In addition to the ambient condition, the Heating, ventilation and air conditioning (HVAC) system’s mode of use can affect the windows’ performance, especially when multiple modes are used in combination (mixed-mode). Although many studies have investigated the mixed-mode application, their conditions for starting/shutting down HVAC equipment and controlling window ventilation are inconsistent with the relevant codes. Here, we propose a mixed-mode operation that resolves the gap between the air conditioning operation temperature and the adaptive comfort upper temperature. It investigates residential buildings’ indoor thermal environment and energy efficiency by combining the effective ventilation opening area ratio (REV) and shading design. Simulation results show that our mixed-mode can reduce the indoor overheating hours by about 50% and the building’s energy consumption by about 50%. We thereby conclude that the openable area of exterior windows in residential buildings in Chongqing should not be less than 10% of the room’s floor axis area where the exterior windows are located. In general, our study expands the existing knowledge of passive energy-saving measures and provides a method for further research on building energy design in hot summer and cold winter regions.

Modelling and mitigating lifetime impact of building demand responsive control of heating, ventilation and air-conditioning systems

This paper presents a modelling methodology to characterize heating, ventilation and air-conditioning (HVAC) equipment lifetime impact of load controls and an aging-aware demand responsive control strategy for single-stage HVAC systems to strike a balance between the electric utility and HVAC life-cycle costs. To assess the control performance and evaluate potential trade-offs between energy consumption, utility cost and equipment lifetime impact, whole-month simulation tests for a single-zone office building have been conducted for the proposed aging-aware control strategy along with two benchmarking strategies – energy minimizing and utility cost minimizing controllers. Test results show that the aging-aware demand response strategy could result in reductions of HVAC equipment aging effect by 18.8% to 39.1% compared to the utility-priority controller. The total building operation cost, with the electricity utility and HVAC life-cycle costs combined, could be reduced by up to 13.2% compared to the utility minimizing strategy and by up to 16.1% compared to the energy minimization baseline.

Review of predictive maintenance algorithms applied to HVAC systems

Predictive maintenance is a preventive maintenance approach that is performed based on an online health assessment and allows for timely pre-failure interventions. It can diminish the cost of maintenance by reducing the frequency of maintenance as much as possible to avoid unplanned reactive maintenance, without incurring the costs associated with too frequent preventive maintenance. The main objective of predictive maintenance of heating, ventilation, and air conditioning (HVAC) systems is to predict when the HVAC equipment failure may occur. The benefits are numerous: planning of maintenance before the failure occurs, reduction of maintenance costs, and increased reliability. For this, the predictive maintenance of the HVAC systems is based on the historical data of the system for predicting the state of health of the system. The process of predictive maintenance application is composed of the Internet of Things (IoT) sensors that are installed inside the HVAC system, then the IoT platforms that help in collecting the signals coming from the sensors and converting them to existing databases. Afterward, the algorithms of application of predictive maintenance could be either knowledge-based approaches, physics-based approaches, or even data-driven-based approaches. A systematic literature review on the existing algorithms of HVAC predictive maintenance application is conducted in this paper to summarize the most used approach for predicting future failures in HVAC systems and to explain the benefits and limits of these algorithms.

Improving operation management performance of air conditioners in offices using data envelopment analysis

Heating, ventilation, and air conditioning (HVAC) systems account for the bulk of a building's energy use, which means that potentially significant energy savings can be made by efficiently modifying how those systems are operated. Although several studies have demonstrated that optimizing the operation of HVAC equipment in combination with sensor readings can reduce potentially wasteful processes, such efforts are limited to buildings that have or can incorporate additional sensors. As an alternative approach, this paper aims to identify potentially wasted energy and provide suggested improvements for buildings that do not have installed sensors, which is important because such suggestions can extend the scope of energy reduction measures. More specifically, we hypothesized that comparing differences in user operations would make it possible to identify potentially wasteful operating times. Accordingly, we began our study by identifying target days and days for improvement using data envelopment analysis and then detected operational differences by comparing their operating times. Our new method was then applied to variable refrigerant flow systems in an office building as a case study. The obtained results showed that the potential for energy reduction was about 7.5%. These results, which helped determine the appropriate times for automatically shutting down each indoor unit, proved effective in implementing energy reduction measures. The novelty of this paper is that it clarified the energy waste that can be detected based on operating times, which is expected to lead to the development of additional options for energy reduction measures, thereby allowing building owners to implement more effective actions based on the sizes and conditions of their buildings.

HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings

Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

Gray Box Time Variant Clogging behaviour and Pressure Drop Prediction of the Air Filter in the HVAC System

Identification and prediction of clogging behavior in heating, ventilation, and air conditioning (HVAC) filters is crucial to avoid issues such as system overheating, energy waste, lower indoor air quality, etc. Researchers are focusing more on the particle loading characteristics of a filter medium in a laboratory environment under steady-state conditions, fixed particle concentrations, area of porosity, dust feed and volumetric flow rate. However, recent research still shows uncertainties in modeling as well as the implementation problems of constructing the HVAC laboratory test bench and equipment. In addition, subjects such as non-uniform particle deposition depreciation of the condition and various type of mechanical filters such as fibrous, fabric, granular, and membrane filter or electrostatic filters which typically used in HVAC systems perform under some assumptions and still need more research. The studies become even more difficult acquiring a large number of time-varying and noisy signals. Another approach among studies is data-driven knowing that Building Automation System (BAS) is not equipped with appropriate sensor measuring the clogging, it is needed to drive the clogging mathematical model from the pressure drop signal. This paper bridges the gap between particle-size study and black box modeling of HVAC filter which has not received much attention from authors. The proposed method assumes that the pressure drop is the result of two time-varying functions; f(t), which represents the dynamics of clogging and, g(t), which refers to dynamics of remained terms. The exponential and polynomial of second order functions are proposed to express the clogging behavior. The software package based on Particle Swarm Optimization Artificial Bee Colony (PSOABC) algorithm, is developed and implemented to estimate the coefficients of the clogging functions based on smallest RMSE, high coefficient of correlation and acceptable tracking. Five Air Handling Unit (AHUs) are selected for practical verification of the model and the results show that the applied method can successfully predict clogging and pressure drop behaviour of HVAC filters.

Fouling of Polymeric Hollow Fiber Heat Exchangers by Air Dust.

Currently, liquid-to-gas heat exchangers in buildings, domestic appliances and the automotive industry are mainly made of copper and aluminum. Using plastic instead of metal can be very beneficial from an economic and environmental point of view. However, it is required that a successful plastic design meets all the requirements of metal heat exchangers. The polymeric hollow fiber heat exchanger studied in this work is completive to common metal finned heat exchangers. Due to its unique design (the use of thousands of thin-walled microtubes connected in parallel), it achieves a high level of compactness and thermal performance, low pressure drops and high operation pressure. This paper focuses on an important aspect of heat exchanger operation—its fouling in conditions relevant to building and domestic application. In heating, ventilation and air conditioning (HVAC) and automotive and domestic appliances, outdoor and domestic dust are the main source of fouling. In this study, a heat exchanger made of polymeric hollow fibers was tested in conditions typical for indoor HVAC equipment, namely with the 20 °C room air flowing through the hot water coil (water inlet 50 °C) with air velocity of 1.5 m/s. ASHRAE test dust was used as a foulant to model domestic dust. A polymeric heat exchanger with fibers with an outer diameter of 0.6 mm (1960 fibers arranged into 14 layers in total) and a heat transfer area of 0.89 m2 was tested. It was proven that the smooth polypropylene surface of hollow fibers has a favorable antifouling characteristic. Fouling evolution on the metallic heat transfer surfaces of a similar surface density was about twice as quick as on the plastic one. The experimental results on the plastic heat exchanger showed a 38% decrease in the heat transfer rate and a 91% increase in pressure drops after eighteen days of the experiment when a total of 4000 g/m2 of the test dust had been injected into the air duct. The decrease in the heat transfer rate of the heat exchanger was influenced mainly by clogging in the frontal area because the first layers were fouled significantly more than the deeper layers.

Performance Analysis of an Integrated Solar Dehumidification System with HVAC in A Typical Corner Store in the USA

Food deserts have emerged in underserved urban and rural areas throughout the United States. Corner markets have filled the food voids, but generally without offering residents access to healthy food. The economics for doing so are prohibitive. The purpose of the study is to investigate an opportunity for reducing corner store energy costs in order to make possible retail of fresh produce and meat. Given the typical dominance of refrigeration to the energy cost in such stores, an integrated solar dehumidification system with heating, ventilation, and air conditioning (HVAC) is considered. A typical corner store baseline reliant upon conventional refrigeration and HVAC equipment is defined to serve as a basis for comparison. MATLAB Simulink dynamic models are developed for the posed system and baseline model. The results show energy reduction in the refrigerated cabinets of maximally 28%, 27%, and 20%, respectively, in Dayton, OH, Phoenix, AZ, and Pine Bluff, AR. The respective HVAC energy savings are respectively 28%, 56%, and 4%. Collectively these correspond to total annual energy savings of 43%, 51%, and 53%, translating to annual energy cost savings of greater than $12K in all locations.

Net-zero energy building design and life-cycle cost analysis with air-source variable refrigerant flow and distributed photovoltaic systems

Net-zero energy building (NZEB) design has been gaining widespread acceptance as a major driver to reach feasible long-term goals of energy reductions in a building sector for the coming decades. Accordingly, net-zero energy targets for commercial buildings have been specified by federal and/or local government agencies in U.S. Although moving toward NZEB goals has technically feasible long-term advantages, economic issues are one of primarily concerns by building owners and designers for the application of advanced building technologies in NZEB design of their retrofit or new building construction. However, the lack of life cycle cost (LCC) data has still hindered reliable understanding of adopting NZEB design by building owners in a cost-effective manner. In response to this gap, this study conducts life-cycle cost analysis (LCCA) of NZEB design with an energy-efficient heating, ventilation, and air conditioning (HVAC) system and a solar power generation system. To enable NZEB design, an air-source VRF heat pump (HP) type system is considered for energy-efficient HVAC equipment in an office building model with a grid-tied photovoltaic (PV) system. Results reveal that the LCC of NZEBs varies primarily due to a combined effect of annual site EUIs, on-site power generation, and cost-items of each location in U.S. climates. Some hot and mild climate zones in the U.S. represent that LCC values of NZEBs are considerably lower. In contrast, some other cold zones result in unattractive LCC values for NZEBs, caused by a combined effect of relatively lower potential of on-site PV electricity generation and city cost indices. Yet, with subsidized financing by the federal government for PV installation costs, LCC values of NZEBs become significantly attractive in most climate locations.

Ventilation rates in California classrooms: Why many recent HVAC retrofits are not delivering sufficient ventilation

Previous research has shown that under-ventilation of classrooms is common and negatively impacts student health and learning. To advance understanding of contributing factors, this study visited 104 classrooms from 11 schools that had recently been retrofitted with new heating, ventilation, and air-conditioning (HVAC) units. CO2 concentration, room and supply air temperature and relative humidity, and door opening were measured for four weeks in each classroom. Field inspections identified HVAC equipment, fan control, and/or filter maintenance problems in 51% of the studied classrooms. Across 94 classrooms with valid data, average CO2 concentrations measured during school hours had a mean of 895 ppm and a standard deviation (SD) of 263 ppm. Ventilation rates (VRs), estimated using the daily maximum 15-min CO2 in each classroom, had a mean of 5.2 L/s-person and a SD of 2.0 L/s-person across 94 classrooms. Classrooms with economizers, with or without demand control ventilation (DCV), tended to have lower mean CO2. Improperly selected equipment, lack of commissioning, incorrect fan control settings and maintenance issues (heavily loaded filters) were all associated with under-ventilation in classrooms. Many classrooms in this sample were frequently too warm to support learning. There were 23 out of 103 classrooms that had indoor air temperature above 25.6 °C for more than 20% of the school hours. Better oversight on HVAC system installation and commissioning are needed to ensure adequate classroom ventilation. Periodic testing of ventilation systems and/or continuous real-time CO2 monitoring (either as stand-alone monitors or incorporated into thermostats) is recommended to detect and correct ventilation problems.

CFD Analysis of Auditorium using Ansys Fluent

The primary aspect of any building design and management is heating, ventilation and air conditioning (HVAC). Such systems play very important role in building construction and then the comfort of the occupants of buildings. Hence proper design of such HVAC system is necessary and is essential for efficient and green buildings the HVAC equipment perform the duty of heating and/ or cooling for residential and commercial buildings. Such HVAC system also provide fresh outdoor air to dilute the air contaminants such as odor from occupants of buildings, volatile organic compounds , chemicals etc. Air conditioning equipment is one of the major components in HVAC system. In the project work, an effort has been made to analyses the HVAC system used in seminar halls of which have sitting capacity of 100 people. It is very much essential to have comfortless for people participating in events like seminar, conferences, commercial presentations in seminar hall. Good cooling of seminar hall is essential especially in summer season and moderate warmness is necessary in winter season. In sitting arrangements, the 10 chairs are arranged in 10 rows. The Computational Fluid Dynamic analysis of HVAC system available in seminar hall is carried out by using ANSYS FLUENT software both summer and winter seasons. Parameter studies have been carried out by varying inlet velocity of air in the range 0.1 to 0.5 m/s. the results have been presented in the form of velocity, pressure and temperature contours. As it is observed that as inlet air velocity increases from 0.1 to 0.5 m/s. the outlet temperature decreases from 307 to 302K.

Heat collection in an attached sunspace

The attached sunspaces are classic bioclimatic systems. Normally, they are used as solar passive techniques in buildings. However, it has been suggested that they could also be used as active systems in order to collect solar energy and harness it in the heating, ventilation and air conditioning (HVAC) equipment. This paper analyses the sunspace built during the ARFRISOL project, which has a validated simulation tool of its thermal behaviour, developed in a previous research. In this paper, this tool is applied to study the possibility of the energy collection. Although this energy is not enough to be used as direct heating system, it can pre-heat the air before entering the HVAC equipment. Two configurations have been studied: collecting the energy during the whole day or only at night to take advantage of the sunspace thermal inertia. Also, the results obtained with these configurations have been compared with the sunspace used in passive mode. For this analysis, several characteristic days with summer and winter climatic conditions have been considered. Finally, an economic evaluation of this innovative system –a solar active sunspace- is performed taking into account the results of the different configurations.

Development of an office tenant electricity use model and its application for right-sizing HVAC equipment

As a consequence of considerable uncertainty about occupancy, occupant behaviour, and the corresponding effect on thermal loads in buildings, it is difficult to correctly size heating, ventilation, and air-conditioning (HVAC) equipment. Mechanical engineers avoid liability of potential under-capacity and corresponding thermal discomfort by making conservative assumptions about occupants. Meanwhile, there has been a surge in research on characterizing occupants through increasingly advanced modelling approaches to support building performance simulation, but these have focused on agent-based models representing individual occupants, which may be impractical for building-level HVAC equipment sizing. This paper describes the development of a data-driven stochastic tenant model using 15 months of data from 17 independent commercial tenants. The model is implemented in EnergyPlus to examine its potential for an improved HVAC equipment-sizing procedure. The results show: the standard schedules are reasonable though conservative; oversizing equipment does not greatly improve comfort; and the tremendous importance of modelling inter-tenant diversity.

Study on Variation of Internal Heat Gain in Office Buildings by Chronology

The additional process of load calculation during the retrofit of heating, ventilation, and air conditioning (HVAC) equipment has been generally omitted in Korea, in recent years. Instead, a simple replacement of HVAC equipment, based on existing HVAC capacities, has been used, which is limited when taking the variation of internal heat gain into consideration, due to changes in the consumption power due to light and office automation (OA) equipment in recent years. Thus, this study aimed to identify a change in internal heat gains over 30 years, from 1985 to 2015, study investigated actual measurement-based academic papers and catalogs about OA equipment, lighting, and the number of occupants per unit area. The heat load produced from OA equipment has increased by approximately 49.6% in 2015 compared to that of 1980, and the heat load from lighting equipment has decreased by 47% compared to that of fluorescent-based lighting, and the heat load from human bodies was approximately 10 W/m2 on average, which showed a minimal change over the past 30 years. The internal heat gain calculated for 30,000 m2 of total floor area has increased constantly for the last 30 years. Note that the internal heat gains were designed excessively, as the values used in literature was approximately 17% to 50% smaller than the value used normally in the case of pre-2000 designs, and the values used after 2000 were approximately 35% to 50% smaller than the design standard value.