Search Results (13)

Earth–air heat exchangers (EAHXs) use the soil’s thermal capacity to dampen the amplitude of outdoor air temperature oscillations. This effect can be used in hot and dry climates for room cooling with no or very little need for resources other than those used during the EAHX construction, an obvious advantage compared to the significant operational costs of refrigeration machines. Contrary to the streamlined process applied in conventional HVAC design (using refrigeration machines), EAHX design lacks straightforward and well-established rules; moreover, EAHXs struggle to achieve office room design cooling demands determined with conventional indoor thermal environment standards, hindering designers’ confidence and the wider adoption of EAHXs for standalone room cooling. This paper presents a graph-based method to assist in the design of a large-diameter EAHX. One year of post-occupancy monitoring data are used to evaluate this method and to investigate the performance of a large-diameter EAHX with up to 16,000 m³/h design airflow rate. Considering an adaptive standard for thermal comfort, peak EAHX cooling capacity of 28 kW (330 kWh/day, with just 50 kWh/day of fan electricity consumption) and office room load extraction of up to 22 kW (49 W/m²) provided evidence in support of standalone use of EAHX for room cooling. A fair fit between actual EAHX thermal performance and results obtained with the graph-based design method support the use of this method for large-diameter EAHX design. Full article

Metal–organic frameworks (MOFs) have garnered significant attention in recent years for their potential to revolutionize heat exchanger performance, thanks to their high surface area, tunable porosity, and exceptional adsorption capabilities. This review focuses on the integration of MOFs into heat exchangers to enhance heat transfer efficiency, improve moisture management, and reduce energy consumption in Heating, Ventilation and Air Conditioning (HVAC) and related systems. Recent studies demonstrate that MOF-based coatings can outperform traditional materials like silica gel, achieving superior water adsorption and desorption rates, which is crucial for applications in air conditioning and dehumidification. Innovations in synthesis techniques, such as microwave-assisted and surface functionalization methods, have enabled more cost-effective and scalable production of MOFs, while also enhancing their thermal stability and mechanical strength. However, challenges related to the high costs of MOF synthesis, stability under industrial conditions, and large-scale integration remain significant barriers. Future developments in hybrid nanocomposites and collaborative efforts between academia and industry will be key to advancing the practical adoption of MOFs in heat exchanger technologies. This review aims to provide a comprehensive understanding of current advancements, challenges, and opportunities, with the goal of guiding future research toward more sustainable and efficient thermal management solutions. Full article

In order to reduce the contribution of the building sector to global greenhouse gas emissions and climate change, it is important to improve the building performance through retrofits from the perspective of carbon emission reductions. Data-driven methods are now widely used in building retrofit research. To better apply data-driven techniques in low-carbon building retrofits, a better understanding is needed of the connections and interactions in optimization objectives and parameters, as well as optimization methods and tools. This paper provides a bibliometric analysis of selected 45 studies, summarizes current research hotspots in the field, discusses gaps to be filled, and proposes potential directions for future work. The results show that (1) the building-performance optimization (BPO) process established through physical simulation methods combines the site, retrofit variables, and carbon-related objectives, and the generated datasets are either directly processed using multi-objective optimization (MOO) algorithms or trained as a surrogate model and iteratively optimized using MOO methods. When a sufficient amount of data is available, data-driven methods can be used to develop mathematical models and use MOO methods for performance optimization from the perspective of building carbon emission reductions. (2) The benefits of retrofits are maximized by holistically taking environmental, economic, and social factors into account; from the perspectives of carbon emissions, costs, thermal comfort, and more, widely adopted strategies include improving the thermal performance of building envelopes, regulating HVAC systems, and utilizing renewable energy. (3) The optimization process based on data-driven methods, such as optimization algorithms and machine learning, apply mathematical models and methods for automatic iterative calculations and screen out the optimal solutions with computer assistance with high efficiency while ensuring accuracy. (4) Only 2.2% and 6.7% of the literature focus on the impacts of human behavior and climate change on building retrofits, respectively. In the future, it is necessary to give further consideration to user behaviors and long-term climate change in the retrofit process, in addition to improving the accuracy of optimization models and exploring the generalization and migration capabilities of surrogate models. Full article

This paper investigates the microclimate in a conference room with thermal stratification, taking as a case study the chapel of Villa San Saverio, now the seat of the “Scuola Superiore” of the University of Catania (Italy). Surveys of the former chapel were conducted to monitor air temperature and relative humidity. Subsequently, the investigation relied on numerical simulations of a simplified computational fluid dynamics (CFD) model built with the DesignBuilder v7.0 software and validated by comparison with measured values. Simulations were then carried out considering three different scenarios: the current state without any HVAC system and two possible HVAC system configurations providing both air conditioning and ventilation. The results show that, from a comfort perspective, a lightweight radiant floor heating system, assisted by an appropriate ventilation system for air renewal placed at the floor level near the occupants, is preferable to floor-level fan coils and high ventilation channels. Furthermore, this was also confirmed by a preliminary energy analysis of the two HVAC options, where the ventilation effectiveness of the winter period, the temperature of the water the emitters are fed, the consequent COP value of the heat pump, and the electricity consumption were taken into consideration. Full article

As the field of zero energy building design and research continues to progress, the use of data analysis methods is on the rise. These methods are applied to create assessment criteria, compare performance, and aid in design decision making. Decision trees, as a data-driven approach, offer interpretability and predictability, assisting designers in summarizing their design experience and serving as a foundation for design references. However, the current application of decision tree methods in the zero energy house sector primarily focuses on HVAC systems, lacking a comprehensive exploration from an architectural design perspective. Therefore, this study presents an empirical method for building and applying models based on decision trees, using zero energy house cases in severely cold regions of China as samples. Through an analysis of the interactions among various passive design parameters and the use of EnergyPlus for performance simulations, a decision tree model is established. This model aids in determining the recommended combinations of passive design parameters that meet the criteria of low energy consumption. Moreover, feature weighting highlights the most influential passive design parameters on building energy consumption, including the length of the architectural gestalt plane, the roof shape, and the ground thermal resistance. This research provides valuable methods and guidance for the design and construction of zero energy houses in severely cold regions of China. Full article

Advances towards smart ecosystems showcase Internet of Things (IoT) as a transversal strategy to improve energy efficiency in buildings, enhance their comfort and environmental conditions, and increase knowledge about building behavior, its relationships with users and the interconnections among themselves and the environmental and ecological context. EU estimates that 75% of the building stock is inefficient and more than 40 years old. Although many buildings have some type of system for regulating the indoor temperature, only a small subset provides integrated heating, ventilation, and air conditioning (HVAC) systems. Within that subset, only a small percentage includes smart sensors, and only a slight portion of that percentage integrates those sensors into IoT ecosystems. This work pursues two objectives. The first is to understand the built environment as a set of interconnected systems constituting a complex framework in which IoT ecosystems are key enabling technologies for improving energy efficiency and indoor air quality (IAQ) by filling the gap between theoretical simulations and real measurements. The second is to understand IoT ecosystems as cost-effective solutions for acquiring data through connected sensors, analyzing information in real time, and building knowledge to make data-driven decisions. The dataset is publicly available for third-party use to assist the scientific community in its research studies. This paper details the functional scheme of the IoT ecosystem following a three-level methodology for (1) identifying buildings (with regard to their use patterns, thermal variation, geographical orientation, etc.) to analyze their performance; (2) selecting representative spaces (according to their location, orientation, use, size, occupancy, etc.) to monitor their behavior; and (3) deploying and configuring an infrastructure with +200 geolocated wireless sensors in +100 representative spaces, collecting a dataset of +10,000 measurements every hour. The results obtained through real installations with IoT as a learning factory include several learned lessons about building complexity, energy consumption, costs, savings, IAQ and health improvement. A proof of concept of building performance prediction based on neural networks (applied to CO₂ and temperature) is proposed. This first learning shows that IAQ measurements meet recommended levels around 90% of the time and that an IoT-managed HVAC system can achieve energy-consumption savings of between 10 and 15%. In summary, in a real context involving economic restrictions, complexity, high energy costs, social vulnerability, and climate change, IoT-based strategies, as proposed in this work, offer a modular and interoperable approach, moving towards smart communities (buildings, cities, regions, etc.) by improving energy efficiency and environmental quality (indoor and outdoor) at low cost, with quick implementation, and low impact on users. Great challenges remain for growth and interconnection in IoT use, especially challenges posed by climate change and sustainability. Full article

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building. Full article

Artificial intelligence (AI) technologies have developed rapidly since 2000. Numerous academic papers have been published regarding energy efficiency improvements for air-conditioning systems. This study reviewed 12 review papers and selected 85 specific cases of applications of AI for HVAC energy usage reduction. In addition to academic studies, 31,221 patents related to HVAC energy-saving equipment filed by 11 companies were investigated. In order to analyze the large amount of data, this study developed a resource description framework (RDF) as an analysis tool. This tool was used with a natural language processing (NLP) program to compare the contents of academic papers and patents. With the automated analysis program, this study aimed to link academic research and corporate research and development, mainly the enterprise patent applications, to analyze the reasons why AI can effectively save energy. This represents a complete analysis of the current status of academic and industrial development. Six methods were identified to save energy effectively, including model-based predictive control (MPC), thermal comfort control, model-free predictive control, control optimization, multi-agent control (MAC), and knowledge-based system/rule set (KBS/RS)-based control. The energy savings of these methods were quantified to be 8.8–25.5%. These methods are widely covered by the examined corporate patent applications. After using NLP to retrieve patent keywords, the landscapes of enterprise patents were constructed and the future research directions were identified. It is concluded that 10 topics, including novel neural network designs, smartphone-assisted machine learning, and transfer learning, can be used to increase the energy-saving effects of AI and enable sustainable air-conditioning systems. Full article

In the building operation phase, the Heating, Ventilation, and Air-Conditioning (HVAC) equipment are the main contributors to excessive energy consumption unless proper design and maintenance is carried out. Moreover, HVAC problems might have an impact on occupants’ discomfort in thermal comfort. Hence, the identification of the root cause of HVAC problems is imperative for facility managers to plan preventive and corrective maintenance actions. However, due to the complex interaction between various equipment and the lack of data integration among Facility Management (FM) systems, they fail to provide necessary information to identify the root cause of HVAC problems. Building Information Modelling (BIM) is a potential solution for maintenance activities to address the challenges of information reliability and interoperability. Therefore, this paper presents a novel conceptual model and user-centric framework to determine the causes of HVAC problems implemented in BIM for its visualization. CMMS and BMS data were integrated into BIM and utilized by the framework to analyze the root cause of HVAC problems. A case study in a university building was used to demonstrate the applicability of the approach. This framework assists the FM team to determine the most probable cause of an HVAC problem, reducing the time to detect equipment faults, and providing potential actions to solve them. Full article

A hybrid air conditioning system, which is composed of a novel gravity-assisted heat pipe (GAHP)-based indirect evaporative cooler (IEC) and direct expansion (DX) cooling coil, is proposed and investigated here. After developing a mathematical model to describe the performance of the GAHP-based IEC, the hybrid system is evaluated during the cooling design day for providing thermal comfort for an office building in Poland. The results are obtained and compared with the combination of a rotary heat exchanger (RHE) and DX cooling coil as the conventional hybrid system. The comparison is performed by analyzing cooling capacity, electricity consumption, and coefficient of performance profiles, which describe the technical, energy, and efficiency aspects, respectively. The results show that the GAHP-based IEC hybrid system is able to enhance the energy performance significantly compared to the conventional one. The proposed hybrid HVAC system improves COP by 39.2% and reduces electricity consumption by 45.0%, according to the design-day of 24 August and the outdoor temperature of 30 °C. As a result, the total operating cost for the assumed cooling season is reduced by 51.7%. Full article

This paper proposes an energy-saving strategy with assistance from solar thermal compensation for building energy systems. The target of the control strategy was to minimize energy consumption under thermal comfort constraints in buildings. First, the factors influential to indoor temperature in building environments were analyzed. Secondly, the internal and external factors, such as building materials; building orientation; window size; heating, ventilation, and air conditioning (HVAC) facilities; blinding device; solar irradiation; wind speed; and outdoor temperature were used to construct a building model on the platform ENERGYPLUS (E+). A controller aiming to regulate the amount of solar irradiation was developed with the Building Controls Virtual Test Bed (BCVTB) tool. Afterward, the building performance under different strategies was tested by co-simulation using both the computational platforms, E+ and BCVTB. The optimum scheme achieved 30.6% energy savings while meeting the same comfort criterion of its competition strategy. The study verified that the proposed strategy of combined heating, ventilation, and air conditioning and blind control could realize the energy savings and comfort satisfaction at the same time. The proposed method provides a reference to the development of low-/zero-energy building concepts in the field. Full article

Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost. Full article

The rational use of energy has motivated research on improving the energy efficiency of buildings, which are responsible for a large share of world consumption. A strategy to achieve this goal is the application of optimized thermal insulation on a building envelope to avoid thermal exchanges with the external environment, reducing the use of heating, ventilation and air-conditioning (HVAC) systems. In order to contribute to the best choice of insulation applied to an industrial shed roof, this study aims to provide an optimization tool to assist this process. Beyond the thermal comfort and cost of the insulation, some hygrothermic properties also have been analysed to obtain the best insulation option. To implement this optimization technique, several thermo-energetic simulations of an industrial shed were performed using the Domus software, applying 4 types of insulation material (polyurethane, expanded polystyrene, rockwool and glass wool) on the roof. Ten thicknesses ranging from 0.5 cm to 5 cm were considered, with the purpose of obtaining different thermal comfort indexes (PPD, predicted percentage dissatisfied). Posteriorly, the best insulation ranking has been obtained from the weights assigned to the parameters in the objective function, using the technique of the genetic algorithm (GA) applied to multi-criteria selection. The optimization results showed that polyurethane (PU) insulation, applied with a thickness of 1 cm was the best option for the roof, considering the building functional parameters, occupant metabolic activity, clothing insulation and climate conditions. On the other hand, when the Brazilian standard was utilized, rock wool (2 cm) was considered the best choice. Full article