Energy-efficient Heating, Ventilation, And Air Conditioning Research Articles

Review of energy-efficient HVAC technologies for sustainable buildings

This review paper provides a comprehensive examination of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) technologies for sustainable buildings. The focus is on exploring the latest advancements, benefits, challenges, and future prospects in the realm of HVAC systems designed to enhance energy efficiency and promote sustainability in building operations. The review begins with an overview of the importance of energy-efficient HVAC technologies in the context of sustainable building design and operation. It underscores the critical role of HVAC systems in reducing energy consumption, minimizing environmental impact, and enhancing occupant comfort and well-being. The paper delves into various categories of energy-efficient HVAC technologies, including advanced heating and cooling systems, ventilation strategies, and smart controls. Each category is examined in detail, highlighting key features, performance metrics, and real-world applications. Furthermore, the review evaluates the benefits associated with energy-efficient HVAC technologies, such as significant energy savings, reduced operating costs, improved indoor air quality, and enhanced building resilience. It also discusses the potential challenges and barriers to adoption, such as high upfront costs, complexity of implementation, and retrofitting existing buildings. The paper explores emerging trends and innovations in the field of energy-efficient HVAC technologies, including the integration of renewable energy sources, smart sensors and actuators, and predictive maintenance algorithms. These advancements have the potential to further optimize energy performance, increase system reliability, and enable proactive management of HVAC systems. In conclusion, the review emphasizes the importance of energy-efficient HVAC technologies as a cornerstone of sustainable building practices. It underscores the need for continued research, innovation, and collaboration among stakeholders to overcome existing challenges and unlock the full potential of energy-efficient HVAC systems in contributing to a more sustainable built environment. Overall, this comprehensive review provides valuable insights into the current state of energy-efficient HVAC technologies, their applications, benefits, and challenges, while also highlighting opportunities for future advancements and their crucial role in achieving sustainability goals in the building sector.

INTEGRATING SUSTAINABILITY INTO HVAC PROJECT MANAGEMENT: CHALLENGES AND OPPORTUNITIES

As environmental concerns continue to escalate, the integration of sustainability principles into project management practices has emerged as a crucial endeavor across various industries. In the realm of Heating, Ventilation, and Air Conditioning (HVAC) systems, where energy consumption and environmental impact are significant, the incorporation of sustainable practices presents both challenges and opportunities. This review provides an overview of the challenges and opportunities associated with integrating sustainability into HVAC project management. Challenges in integrating sustainability into HVAC project management stem from various factors, including technological limitations, cost considerations, and organizational barriers. Technologically, the adoption of sustainable HVAC solutions often requires investments in advanced equipment and systems, which may not always be readily available or affordable for all projects. Additionally, the upfront costs of implementing sustainable practices can be perceived as prohibitive, especially in industries where budget constraints are prevalent. Moreover, organizational inertia and resistance to change pose significant challenges, as stakeholders may be hesitant to deviate from traditional HVAC methodologies. Despite these challenges, there exist ample opportunities for integrating sustainability into HVAC project management. Advancements in technology, such as the development of energy-efficient HVAC systems and renewable energy integration, provide opportunities to reduce environmental impact while enhancing operational efficiency. Furthermore, the growing emphasis on green building certifications, such as LEED (Leadership in Energy and Environmental Design), incentivizes the adoption of sustainable HVAC practices by offering recognition and financial benefits to projects that meet stringent sustainability criteria. Moreover, the increasing awareness of environmental issues and regulatory requirements underscores the importance of integrating sustainability into HVAC project management. By aligning with regulatory standards and industry best practices, organizations can mitigate risks associated with non-compliance while enhancing their reputation as environmentally responsible entities. While challenges persist, the integration of sustainability into HVAC project management presents significant opportunities for organizations to mitigate environmental impact, enhance operational efficiency, and foster a culture of innovation. By addressing these challenges proactively and leveraging available opportunities, organizations can position themselves as leaders in sustainable HVAC project management.
 Keywords: HVAC, Project Management, Challenges, Sustainability, Review.

THE ROLE OF AGILE PROJECT MANAGEMENT IN DRIVING INNOVATION IN ENERGY-EFFICIENT HVAC SOLUTIONS

This review delves into the significance of Agile Project Management (APM) in fostering innovation within the realm of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) solutions. As the global focus intensifies on sustainability and energy conservation, the HVAC industry stands at the forefront of addressing environmental challenges through innovative technologies. Agile methodologies, originally developed for software development, have transcended their conventional domains to revolutionize project management across various industries. This review explores how the application of Agile principles can catalyze innovation in energy-efficient HVAC solutions. Firstly, it delineates the core tenets of Agile Project Management, emphasizing iterative development, adaptive planning, and customer collaboration. By prioritizing flexibility and responsiveness to change, Agile methodologies empower HVAC project teams to swiftly adapt to evolving customer needs and technological advancements. This agility is particularly crucial in the HVAC sector, where rapid technological innovations necessitate quick iterations and constant feedback loops to refine energy-efficient solutions. Furthermore, the review elucidates how Agile frameworks such as Scrum and Kanban facilitate cross-functional collaboration and enhance communication within HVAC project teams. By fostering a collaborative environment that encourages transparency and knowledge sharing, Agile methodologies cultivate a culture of innovation wherein team members can experiment with novel ideas and swiftly pivot based on market feedback. Moreover, this review underscores the role of Agile Project Management in mitigating risks associated with innovation in energy-efficient HVAC solutions. Through frequent iterations and incremental delivery, Agile methodologies enable HVAC project teams to identify potential bottlenecks and address them proactively, thereby reducing project risks and enhancing overall project success rates. This review highlights the transformative potential of Agile Project Management in driving innovation within the energy-efficient HVAC industry. By embracing Agile methodologies, HVAC project teams can navigate the complexities of innovation more effectively, accelerate time-to-market for energy-efficient solutions, and ultimately contribute to the sustainable transformation of the HVAC sector.
 Keywords: Agile, Project Management, Innovation, Energy-Efficient, HVAC, Review.

District Cooling System Control for Providing Operating Reserve Based on Safe Deep Reinforcement Learning

Heating, ventilation, and air conditioning (HVAC) systems are well proved to be capable to provide operating reserve for power systems. As a type of large-capacity and energy-efficient HVAC system (up to 100 MW), district cooling system (DCS) is emerging in modern cities and has huge potential to be regulated as a flexible load. However, strategically controlling a DCS to provide flexibility is challenging, because one DCS services multiple buildings with complex thermal dynamics and uncertain cooling demands. Improper control may lead to significant thermal discomfort and even deteriorate the power system's operation security. To address the above issues, we propose a model-free control strategy based on the deep reinforcement learning (DRL) without the requirement of accurate system model and uncertainty distribution. To avoid damaging “trial & error” actions that may violate the system's operation security during the training process, we further propose a safe layer combined to the DDPG to guarantee the satisfaction of critical constraints, forming a safe-DDPG scheme. Moreover, after providing operating reserve, DCS increases power and tries to recover all the buildings' temperature back to set values, which may cause an instantaneous peak-power rebound and bring a secondary negative impact on power systems. Therefore, we design a self-adaption reward function within the proposed safe-DDPG scheme to constrain the peak-power effectively. Numerical studies based on a realistic DCS demonstrate the effectiveness of the proposed methods.

Impact of Building Automation on Indoor Air Quality and HVAC Performance

In response to the growing emphasis on healthier indoor environments and sustainable building practices, this research delves into the intricate relationship between building automation and key facets of indoor environmental quality. Specifically, the study focuses on the impact of building automation systems on Indoor Air Quality (IAQ) and the overall performance of Heating, Ventilation, and Air Conditioning (HVAC) systems. With the increasing demand for enhanced IAQ, building automation emerges as a crucial tool for real-time monitoring and optimization of various IAQ parameters. The research explores the comprehensive integration of automation technologies within building systems, elucidating their multifaceted effects on IAQ. By scrutinizing the intricate interplay between automation systems and HVAC components, the study unveils the ways in which automation technology influences and contributes to IAQ management. The investigation encompasses a thorough examination of sensor technologies, control algorithms, and data analytics employed in building automation to ensure a nuanced understanding of their impact on IAQ. Furthermore, the study elucidates the broader implications of building automation on HVAC system performance. It investigates how automated control mechanisms, coupled with real-time data analytics, contribute to the efficient operation of HVAC systems in maintaining optimal IAQ. The research contemplates the synergies between automation technologies and HVAC components, emphasizing their role in not only ensuring healthier indoor environments but also in achieving energy-efficient and sustainable HVAC operation.

Multi-objective optimization of building HVAC operation: Advanced strategy using Koopman predictive control and deep learning

Predictive control is an effective method for addressing the increasing demand for heating, ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and efficiency. In this paper, the multi-objective problem of energy-efficient HVAC operation while tracking desired setpoints is addressed through an advanced control strategy, including a novel supervisory data-driven predictive control (DPC) and a local loop with a PI controller. The supervisory level is based on the integration of the DPC framework and the bilinear Koopman predictor. A deep neural network architecture is developed to realize the bilinear building thermal predictor. In addition, the integral input-to-state stability (iISS) constraint is enforced in the training procedure to obtain a global iISS control-oriented thermal predictor that enables the application of predictive control purely based on historical data. The effectiveness of the proposed approach is demonstrated through the simulation of a three-zone office in the transient system simulation software (TRNSYS). The proposed control configuration is implemented using a coupled TRNSYS-MATLAB simulation framework. It has shown significant potential for keeping the desired environment in line with energy conservation. Furthermore, a comparison is conducted between the proposed approach, the conventional controller, and another recent DPC approach, which demonstrates the suggested method’s superiority.

Multi-indicator adaptive HVAC control system for low-energy indoor air quality management of heritage building preservation

Regenerated commercial heritage buildings employ heating, ventilation, and air conditioning (HVAC) systems that significantly increase their energy consumption, necessitating a methodology that supports low-energy operation to achieve a sustainable built environment. Poor indoor air quality (IAQ) management can cause irreversible damage to heritage buildings. However, there are risks of the destruction of inherent heritage values if energy-efficiency approaches are implemented without considering the visual impacts and multiple IAQ parameters in heritage buildings. To preserve heritage buildings, this study developed a multi-indicator adaptive ventilation control system for IAQ management using digital twin technology, which consisted of triggers and feedback. A digital representation of heritage buildings was established using Heritage Building Information Modelling (HBIM) with sensors to trigger adjustments in ventilation system settings. The sensor placement rules for IAQ monitoring and HVAC control of heritage buildings were demonstrated using computational fluid dynamics (CFD) simulations. The relationships among the HVAC inlet velocity, multiple IAQ parameters, and energy consumption were quantified using CFD and energy simulations. Simulation data were used to generate responsive charts for adaptive ventilation control, and the sensor data provided feedback to the ventilation system. The optimal ventilation system control strategy achieved up to 30% energy savings in the illustrative example. The proposed multi-indicator adaptive HVAC control system contributes significantly to the timely reduction of multiple air pollutants, IAQ parameter adjustments for the preservation of heritage buildings with minimal structural and visual impacts, and the need for more autonomous and energy-efficient HVAC systems.

Artificial intelligence enabled energy-efficient heating, ventilation and air conditioning system: Design, analysis and necessary hardware upgrades

Literature search across different databases showed that the application of artificial intelligence (AI) in heating, ventilation and air conditioning (HVAC) equipment has been extensively studied. On the commercial front, Internet search suggested that numerous AI-equipped HVAC products have been launched. These products apply AI in very different ways, and their energy-saving effects are also different. Such divergence and uncertain energy-saving effects may hinder AI application. To overcome this difference and accelerate the development of AI applications, the present study proposed a double diamond preferred reporting items for systematic reviews and meta-analysis (PRISMA) method—an analysis method that combined literature review with design thinking. Through a process of divergence-convergence-re-divergence, this study described how to design AI functions for energy-efficient HVAC systems, taking into account more than 1,700 research papers it had reviewed. However, there was a limitation on the part re-divergence. Because the vast majority of research papers only published results of successful AI applications, no cases of failed applications were available for review, making it impossible to re-think profoundly. Instead, this study collected raw data from 88 research papers and used these data to analyze the effectiveness and ineffectiveness of AI in depth. It was concluded that AI application must be accompanied by necessary hardware improvements to achieve effective energy savings. AI-enabled energy-saving effects for chillers, air-handing units, heating systems, and air conditioners, as well as corresponding hardware upgrades, were discussed.

Advancements in Smart Thermostat Technology for Enhanced HVAC Energy Management

This study examines the transformative potential of smart thermostat technology in revolutionizing Heating, Ventilation, and Air Conditioning (HVAC) energy management. Through a combination of literature review and empirical analysis, the research explores the significance of energy-efficient HVAC systems in reducing carbon footprint and energy costs. Traditional thermostat limitations, including inflexible scheduling and lack of user engagement, underscore the need for innovative solutions. The evolution of smart thermostat technology is traced from basic programmable thermostats to advanced systems integrated with learning algorithms, occupancy sensing, and the Internet of Things (IoT). Empirical data from surveys, case studies, and energy consumption analysis showcase the tangible impact of smart thermostat adoption. Energy savings of up to 30% in commercial settings and 25% in residential contexts underscore the technology's potential for efficiency. User satisfaction surveys reveal improved comfort levels and satisfaction with remote control capabilities. Challenges such as installation complexities and data security concerns highlight areas for improvement. Data analytics and machine learning emerge as pivotal in enhancing smart thermostat efficiency, contributing to additional energy savings. Comparisons between smart thermostat models underscore the importance of occupancy sensing and remote control features in optimizing energy efficiency and user satisfaction. The study's findings suggest a promising avenue for transforming HVAC energy management towards a more sustainable and user-centric approach

A Review on Virtual Testbed Frameworks for Implementation of Various HVAC Control Strategies

This literature survey provides a review of virtual testbed frameworks for the implementation of various Heating, Ventilation, and Air Conditioning (HVAC) control strategies. With the advancement of technology and the increasing demand for energy-efficient HVAC systems, virtual testbeds have emerged as powerful tools for evaluating and optimizing control strategies. This survey examines the existing literature on virtual testbed frameworks, highlighting their key features, advantages, and limitations and specifically with the frameworks that supports implementation of reinforcement-based control methods and allow implementation of baseline methods for testing various control strategies. It also presents an overview of different HVAC control strategies that have been implemented and evaluated using these frameworks. The survey aims to provide researchers in the field of HVAC control with a valuable resource for understanding the state-of-the-art virtual testbed frameworks and their applications

Energy-Efficient Approaches for a Machine Tool Building in a University through Field Measurement and Energy Modelling

The heating, ventilating, and air conditioning (HVAC) system consumes nearly 50% of the building’s energy, especially in Taiwan with a hot and humid climate. Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment, it is essential to employ an energy-efficient HVAC system. This study conducted the machine tools building in a university. The field measurement was carried out, and the data were used to conduct energy modelling with EnergyPlus (EP) in order to discover some improvements in energy-efficient design. The validation between field measurement and energy modelling was performed, and the error rate was less than 10%. The following strategies were proposed in this study based on several energy-efficient approaches, including room temperature settings, chilled water supply temperature settings, chiller coefficient of performance (COP), shading, and building location. Energy-efficient approaches have been evaluated and could reduce energy consumption annually. The results reveal that the proposed energy-efficient approaches of room temperature settings (3.8%), chilled water supply temperature settings (2.1%), chiller COP (5.9%), using shading (9.1%), and building location (3.0%), respectively, could reduce energy consumption. The analysis discovered that using a well-performing HVAC system and building shading were effective in lowering the amount of energy used, and the energy modelling method could be an effective and satisfactory tool in determining potential energy savings.

On energy-efficient HVAC operation with Model Predictive Control: A multiple climate zone study

This paper aims to quantify the performance of Model Predictive Control (MPC) for a typical commercial building heating, ventilation and air conditioning (HVAC) system across a wide range of climate and weather conditions. The motivation of the study comes from the fact that although there is a large body of work on MPC for HVAC systems, there is a lack of studies that examine the range of possible performance of MPC, in terms of both energy savings and maintaining indoor climate (temperature and humidity) as a function of outdoor weather. A challenge in conducting such a study is developing an MPC controller that can be used in a wide range of weather. The root cause of this challenge is the need for a tractable cooling and dehumidification coil model that can be used by the MPC controller, since the coil may operate in quite distinct modes depending on weather. We present such an MPC controller, and then leverage it to conduct an extensive simulation campaign for fourteen climate zones in the United States and four weather conditions (winter, spring, summer, and fall) in each climate zone. The performance of the proposed controller is compared with not only a rule-based baseline controller but also with a simpler MPC controller that ignores humidity and latent heat considerations. There are several results the arise from this comparative study. One such result is that energy savings from MPC over baseline can vary dramatically based on climate and season. Another is that the effect of ignoring humidity in the MPC formulation can lead to poor indoor humidity control more in milder weather rather than in hot weather. The results from this study can help practitioners and researchers assess costs and benefits of proposed MPC formulations for HVAC control.

Learning Efficient Dynamic Controller for HVAC System

People employ a variety of devices to achieve comfort in various aspects of their lives; for example, numerous types of air conditioners are used to maintain a pleasant indoor temperature. The wide applications of heating, ventilation, and air conditioning (HVAC) systems have significantly improved the comfort level of the indoor environment for commercial buildings. However, air conditioners have their own set of issues, the most significant of which is the increased energy usage. Besides the fact that HVAC is a recent move toward the indoor temperature adjustment, the energy cost that arose from the HVAC system is still high, which takes up over 40% of the total energy. How to develop an energy-efficient HVAC system has become one of the challenging topics that need further investigation. This paper addresses this problem with the aid of deep learning (DL) algorithms. Furthermore, this study proposes an energy-efficient controller and improvement schemes for HVAC systems based on deep reinforcement learning algorithms. To reduce the implementation cost of the proposed design, this study utilizes neural network pruning techniques to develop a low-complexity HVAC controller. The experimental results show that the algorithm proposed in this study not only retains a high energy-saving rate but also reduces the implementation cost of the HVAC control algorithm.

Hygrothermal Performance of Salt (NaCl) for Internal Surface Applications in the Building Envelope.

Salt (NaCl), as a by-product from the potash and desalination industry, can be the solution to the scarcity of building materials and might replace more energy-consuming materials. However, salt carries the risk of deliquescence in humid environments. This study conducted fundamental research on the hygrothermal performance of salt for internal surface applications in the building envelope in six different climate conditions. In addition, salt’s performance was also compared with that of gypsum in similar applications. The simulation models (using WUFI®Pro, WUFI®Plus) and in situ measurements were applied to investigate the hygrothermal consequences of the incorporation of salt on the thermal envelope, indoor environment, and energy consumption. Our studies revealed that salt provided the best hygrothermal responses without Heating, Ventilation, and Air Conditioning (HVAC) in very hot-dry and the worst in very hot-humid climates. With an energy-efficient thermal envelope and HVAC, salt can also find an indoor application in temperate, continental, and subpolar climates. In comparison to gypsum, salt has a slightly higher energy demand (heating, cooling, and dehumidification) due to its higher thermal conductivity and moisture resistance. This study fills the knowledge gap on salt’s hygrothermal performance and shows the potential in its utilization.

Enabling Energy Efficient HVAC Operation Through IWSNs

Many industrial processes rely on specific environmental conditions to ensure product quality and the well-being of human workers. To ensure the required production conditions, typically, energy-intensive Heating, Ventilation and Air Conditioning (HVAC) systems are used. In this paper, we present a variable Industrial Wireless Sensor Network (IWSN) as well as the linked measurement strategy and two different real-world deployments. The sensor nodes can measure environmental conditions such as thermal comfort, air purity, and three-dimensional airflow. For reliable wireless communication, the nodes feature a dual transceiver radio that can operate at sub-1 GHz and 2.4 GHz frequency bands. The collected data is used for a cyber physical production system (CPPS) to provide decision support and enable a demand-driven control of HVAC systems. We show that even with energy savings as low as 5% through our CPPS, up to 3990.56t <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CO_{2}$ </tex-math></inline-formula> -eq could be saved per year in our exemplary real-world deployments.

Stochastic Optimal Control of HVAC System for Energy-Efficient Buildings

The heating, ventilation, and air-conditioning (HVAC) system account for substantial energy use in buildings, whereas a large group of occupants is still not actually feeling comfortable staying inside. This poses the issue of developing energy-efficient HVAC control, i.e., reduce energy use (cost) while simultaneously enhancing human comfort. This brief pursues the objective and studies the stochastic optimal HVAC control subject to uncertain thermal demand (i.e., the weather and occupancy). Particularly, we involve the elaborate predicted mean vote (PMV) thermal comfort model in the optimization. The problem is computationally challenging due to the nonlinear and nonanalytical constraints imposed by the system dynamics and PMV model. We make the following contributions to address it. First, we formulate the problem as a Markov decision process (MDP) which is a desirable modeling technique capable of handling the complexities. Second, we propose a gradient-based learning (GB-L) method for progressively learning a stochastic control policy off-line and store it for on-line execution. Third, we prove the learning method’s converge to the optimal policies theoretically, and its performance (i.e., energy cost, thermal comfort, and on-line computation) for HVAC control via simulations. The comparisons with the existing model predictive control based relaxation (MPC-R) method which is assumed with accurate future information and supposed to provide the near-optimal bounds show that though there exists some discount in energy cost reduction (i.e., 6.5%), the proposed method can enable efficient on-line implementation (less than 1 s) and provide a high probability of thermal comfort under uncertainties.

Utilization of U-shaped finned heat pipe heat exchanger in energy-efficient HVAC systems

Energy-efficient Heating Ventilating and Air Conditioning (HVAC) system utilization is necessary to reduce energy consumption in buildings amid the supply difficulties of energy sources and the environmental impacts of high energy use. This study aims to investigate the utilization of a finned U-shaped heat pipe heat exchanger (HPHE) in a vertical configuration to reduce cooling and reheating energy in an HVAC system. The effectiveness of the U-shaped HPHE, coefficient of performance (COP) of the HVAC system coupled with U-shaped HPHE, and dehumidification capability are presented in this paper. The U-shaped HPHE was installed at the inlet fresh airstream ducts wrapped around the cooling coil, and tests were carried out with one-and two-row configurations arranged in eight heat pipes per row. The fresh air temperature and velocity across the evaporator section were varied at the ranges from 30 to 45 °C and 1.5 to 2.5 m/s, respectively. The results show that the two-row U-shaped HPHE significantly affects the precooling and reheating processes in the HVAC system compared with the one-row configuration. The two-row U-shaped HPHE can enhance the coefficient of performance (COP) by 39.9% over the HVAC without HPHE. The highest energy saving for precooling and reheating was 288.1 and 340.2 W, respectively, at 0.080 m3/s air volume and can reduce relative humidity by 21.6%. The investigation implies that the U-shaped heat pipe succeeded in becoming a solution to an HVAC system that requires cooling to reheat the HVAC system.

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).

COMPARISON OF INDOOR AIR TEMPERATURE AND OPERATIVE TEMPERATURE -DRIVEN HVAC SYSTEMS BY MEANS OF THERMAL COMFORT AND ENERGY CONSUMPTION

The main purpose of Heating, Ventilating and Air-Conditioning (HVAC) systems is to satisfy thermal comfort for the occupants. Conventionally, HVAC systems adjust set-temperature to achieve thermal comfort by continuously measuring indoor air temperature of the environment. However, ASHRAE 55, a standard of acceptable thermal environments, offers to use acceptable ranges of operative temperatures in air-conditioned buildings. Considering operative temperature is a function of indoor air temperature and mean radiant temperature, set-temperature of HVAC system can be controlled by using operative temperature to satisfy neutral thermal comfort for the occupants. This study compares thermal comfort and energy consumption of two exactly same HVAC systems which are operated based on indoor air temperature and operative temperature, respectively. Two office rooms with same architectural configurations -which are located in a university-Ankara-Turkey- were selected as a case study. The HVAC systems were operated based on indoor air temperature and operative temperature, respectively, at the same time and occupancy schedules. The results showed that operative temperature driven controlled HVAC system achieves better thermal comfort while slightly increasing energy consumption. The main findings of this study would be useful not only to design energy-efficient HVAC systems but also create more comfortable environments.

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.