Building Air Conditioning Systems Research Articles

Modelling and management of flexible residential building components using system based heuristic approaches

AbstractThe building energy management system (BEMS) components, that is, the heating, ventilation, and air conditioning (HVAC) systems, refrigerators, and lighting significantly impact energy consumption. This paper presents the modelling, including HVAC and refrigerator with interior heat and external illuminus of lighting for residential buildings. The energy consumption of these components has been examined using eight meta‐heuristic algorithms with weather conditions of five cities in India. The statistical and box‐plot analysis results recommended the original artificial ecosystem algorithm compared to others for energy management. The results of the proposed smart residential building have been compared with the published model results of a residential building with and without BEMS and an economic analysis has also been carried out. The simulation results show that the maximum load has been shifted to off‐peak durations. The energy savings in the proposed BEMS compared to existing BEMS and without BEMS are 34% and 57%, respectively.

A novel high-dimensional sensor calibration framework integrating thermodynamic laws in complex HVAC systems

Accurate calibration of sensors is critical for ensuring energy efficient operation of Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings. Due to the high dimensionality of sensor data and the complexity of multiple-fault scenarios, calibrating sensors in large and complex HVAC systems presents significant challenges. To address this issue, this study introduces a novel sensor calibration framework that integrates thermodynamic laws for high-dimensional sensor calibration in complex HVAC systems. The traditional calibration method heavily relies on accurate data, making it difficult to apply in practical engineering projects. The innovative aspect of our method lies in its integration of thermodynamic laws, such as mass balance and energy conservation, with sensor calibration framework. This approach enables the framework to handle high-dimensional sensor measurements effectively without any training data. We compared five optimization algorithms and applied them to a central cooling system in Hong Kong. The results demonstrated that the simulated annealing (SA) is the most robust for solving the calibration problem, even in scenarios with up to 21 faulty sensors, with the calibrated sensor accuracy meeting the standards for conventional chiller plant operations. This novel framework provides a robust and reliable solution for high-dimensional sensor calibration in large and complex HVAC systems, addressing the growing need for precise sensor calibration as the number of installed sensors increases.

A dynamic maintenance planning methodology for HVAC systems based on Fuzzy-TOPSIS and failure mode and Effect Analysis

Maintenance is critical to the efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings. The conventional maintenance methods are planned for ideal situations and cannot easily be adapted to prevailing management/operational conditions, resulting in increased use of resources and energy. This study developed a dynamic maintenance planning framework for HVAC systems employing Fuzzy-TOPSIS and FMEA and aimed at reducing and stabilising maintenance costs while enhancing system reliability. Fuzzy-TOPSIS technique was used to establish maintenance priorities based on a carefully identified set of criteria, including the risk priority number (RPN), which is adaptable to changing conditions and ensures continuous monitoring. This is a novel application to HVAC systems. A real-world implementation in an HVAC company involving data collection, fuzzification of expert evaluations, priority ranking of components, and development of maintenance ranking validated the framework's efficiency. The running conditions of the HVAC system under the new approach, expressed by the air quality and air leakages (by extension energy saving), showed a significant improvement at the various test points in the 600-h running. The results demonstrated significant cost savings, enhanced system reliability, improved energy efficiency, and better indoor air quality. This dynamic maintenance planning framework offers a concise and adaptable solution for optimising HVAC maintenance operations.

The role of the electricity grid in operation-induced greenhouse gas emissions by a residential building: A multi-year retrospective simulation study

In view of the growing shares of renewables in the electricity grid in combination with the electrification of hvac (heating, ventilation, air conditioning) systems in residential buildings, the grid intensity (in terms of carbon dioxide emissions per unit of electric energy) becomes increasingly sensitive to weather conditions, and synchronicity between weather and the grid becomes a more critical aspect in building performance assessment. Using building performance simulation techniques to seek robust building designs requires awareness about the uncertainties in circumstantial factors that affect performance. This 2016 – 2022 retrospective study highlights the effects of using low or high temporal resolution grid emissions intensity data on projected operation-induced carbon dioxide emissions for a terraced dwelling in the Netherlands. Building fabric quality, the occupant profile, and systems configurations (i.e., hvac and photovoltaics) are varied to investigate the effects of the applied grid model resolution. This study shows that ignoring high-resolution grid intensity data is getting increasingly problematic; applying low resolution (annual) instead of high-resolution (15-minute) grid intensity data leads to an increasingly unjustified optimistic assessment both for net and gross emissions (either or not allowing for carbon displacement by feeding locally generated electricity into the grid).

Experimental performance investigation on a desiccant-assisted two-stage evaporative cooling system in hot and humid areas

The escalating demand for energy-efficient and eco-friendly air conditioning systems in buildings has spurred the development of comfortable, efficient, and cost-effective air handling system configurations. Desiccant cooling has shown a higher coefficient of performance (COP) compared to traditional vapor compression refrigeration (MVCR) systems. However, current desiccant cooling facilities struggle to decouple and efficiently handle indoor heat and moisture loads. The complexity of these facilities has kept intricate cooling systems remaining theoretical, requiring simplification and experimental investigation. This paper presents an experimental study on a compact, two-stage evaporative cooling system assisted by a liquid desiccant. A key innovation of this system is the inclusion of a two-stage heat exchanger that adeptly manages the decoupling of indoor heat and moisture loads under challenging high-temperature and high-humidity conditions. Performance analysis was conducted experimentally under three hot and humid climatic conditions using an optimized operating parameter scheme. Starting from an initial condition of 36.22 °C and a humidity ratio of 0.028 kg/kg, the proposed system achieves COP values ranging from 11.3 to 18.4 without compromising indoor comfort or facility compactness. Under high heat and high humidity conditions, the optimized system extracts 20 % of the product air as the secondary air source. It can reach a minimum supply air temperature of 18.44 °C, achieved under optimized conditions of low wind speed (1.0 m/s), high desiccant concentration (35 %), and substantial desiccant flow (0.09 kg/s). Experimental studies of this new system provide the possibility of achieving high efficiency in air handling over a wide range of temperatures and humidity.

Analysis and prediction of energy consumption in office buildings with variable refrigerant flow systems: A case study

Accurately predicting building air-conditioning energy consumption is particularly important for energy management. However, for small datasets, there is a lack of simple and effective prediction methods applicable to multiple buildings. In order to fully explore the energy consumption mode of Variable Refrigerant Flow (VRF) air conditioning system and improve the accuracy of the model. This study uses the weather data of an outdoor meteorological data collection system and the air conditioning system and energy consumption data of an office building in Jinan. Initial analysis revealed that occupant behavior and daily variations in outdoor air temperatures significantly impact the energy consumption of air conditioning systems. Then, by applying the random forest algorithm and Pearson correlation analysis, the study identified daily average outdoor air temperature and VRF indoor unit running time as critical features for prediction. Subsequently, three machine learning models—Multiple Linear Regression (MLR), Back Propagation Neural Network (BPNN), and Long Short-Term Memory (LSTM) neural network—were developed and assessed using a 4:1 training-to-testing set ratio. Comparative analysis revealed that all models performed robustly, with the MLR model exhibited superior predictive accuracy (R2 (Coefficient of Determination) = 0.98, MAE (Mean Absolute Error) = 0.005 kWh/(m2·day), and RMSE (Root Mean Square Error) = 0.007 kWh/(m2·day) for the testing set) and simplicity. Furthermore, the effectiveness of the MLR model in handling small sample sizes was confirmed through cross-validation of data from two additional office buildings, highlighting its suitability for predicting energy consumption of VRF air conditioning systems in office buildings.

A joint optimization strategy for electric vehicles and air conditioning systems with building battery configuration

Building air conditioning systems, electric vehicles and battery energy storage systems all provide substantial flexibility for grid operations. However, the joint optimization strategy involving these three demand response resources in buildings has been infrequently studied. This research proposes a day-ahead optimization strategy to coordinate the joint operation of air conditioning systems and electric vehicles. In this framework, cooling load is predicted using an autoregressive exogenous model, while electric vehicle charging load is predicted through Monte Carlo simulations. An evaluation method is introduced to assess the comprehensive benefits of the demand response strategy, considering thermal comfort, economic efficiency, and grid friendliness. Using an office building in Tianjin, China, as a case study, the results indicate that the joint optimization strategy reduces operational costs by 3.71 % and peak electricity load by 38.62 % compared to the original strategy. Furthermore, it enhances the grid friendliness of the energy system by 42.64 %. The configuration of the battery energy storage system is also explored in conjunction with the optimization strategy to further improve grid friendliness. The impact of economic factors and thermal comfort on the configuration of the battery energy storage system is discussed. In the case study, raising the upper temperature limit by 1 °C can save at least 17.1 % in capacity, while battery energy storage system investment and operational costs can respectively be reduced by 55.04 % and 27.14 % within the thermal comfort range.

Development of an FDD model for an existing building using transfer learning

Building heating, ventilation, and air conditioning (HVAC) systems are affected by several errors that can cause thermal discomfort to occupants and waste energy in buildings. Therefore, early and accurate Fault Detection and Diagnosis (FDD) is vital for enhancing the system’s efficiency. Data-driven FDD is promising because it is convenient compared to the first-principles-based rule set that demands in-depth expertise. However, to realize data-driven FDD for real-life cases, the data-imbalance problem in FDD must be solved. In this study, the authors suggest a novel approach that generates synthetic data from a building system simulation tool, HVACsim+, using them as a source model to apply transfer learning to a target air handling unit system. Even though from the existing target system only the normal operational data were available, the transfer learning approach was satisfactory, confirming that the proposed method effectively mitigates data imbalance in developing data-driven FDD.

Energy consumption analysis and operating characteristics research on small time scale of variable refrigerant flow air conditioning systems in public buildings

In order to analyze the energy consumption and operation characteristics of the variable refrigerant flow (VRF) air-conditioning system on small time scale and put forward effective energy-saving suggestions, the VRF system operation experiments are carried out under different indoor set temperatures, different indoor unit opening quantities and different indoor set air velocities. This experiment studies the main influencing factors of energy consumption and operation of the VRF system, such as indoor temperature difference, indoor air velocity, outdoor environment temperature and so on. The results show that the rated power of indoor unit is linearly related to the energy consumption of the VRF system. The total active power changes greatly when the VRF starts and stops, and the outdoor ambient temperature changes. The indoor set air velocities have a great influence on the energy consumption and operation of the VRF system. The performance parameters increase first and then decrease with the increase of compressor frequency and load rate. When the load rate is 40 %∼60 %, the performance parameters can reach 3.2 ∼ 4.2.

Integration of Air Conditioning Systems in Intelligent Buildings to Increase Efficiency

Heating, ventilation, air conditioning, and cooling, collectively known as HVAC, account for more than 40% of the electricity consumed in buildings. Global climatic factors, referring to comfort and factors related to health, dictate that there is an increasing need for the application of HVAC/R systems. Therefore, it is increasingly compelling to investigate various approaches to enhance the effectiveness of air treatment systems. The automation of buildings has facilitated the integration of air treatment systems with “other existing” systems that can impact the indoor climatic conditions, such as automated windows and curtains, etc. In order to achieve this, it is essential to employ calculation techniques for air handling systems and for the heat transfer between buildings and the external environment. By integrating these systems, it becomes possible to maintain a comfortable environment while actively minimizing the electricity consumption of the heat pump. This article focuses on the integration of air conditioning systems in intelligent buildings to enhance efficiency.

Thermal Performance Improvement of Composite Phase-Change Storage Material of Octanoic Acid–Tetradecanol by Modified Expanded Graphite

Phase-change cold storage technology is recommended as a solution for energy conservation and carbon neutrality in air conditioning systems of buildings. This study focuses on the development of binary composite phase-change materials comprising octanoic acid–tetradecanol (OA-TD). To enhance its thermal conductivity, expanded graphite (EG) was employed as an additive carrier, and the surface modification of EG particles using hexadecyltrimethoxysilane (HDTMOS) was attempted to make up for the instability and further to improve the performance of OA-TD/EG CPCMs. The OA-TD/EG-HDTMOS CPCMs were synthesized by EG mixed with EG-HDTMOS at a 1:1 mass ratio. The thermal performance and stability of the OA-TD/EG-HDTMOS CPCMs were thoroughly evaluated by multi-cycle melting–solidification and thermal conductivity measurements. The results revealed that the OA-TD mixture, when at a mass ratio of 77:23, exhibited a phase-transition temperature of 11.4 °C and a latent heat ranging from 150 to 155 J/g. Then, the OA-TD/EG-HDTMOS composite material, at a 12:1 mass ratio of OA-TD to EG-HDTMOS, solidified and melted at temperatures of 9.2 °C and 11.2 °C, with a latent heat ranging from 138 to 143 J/g, and significantly improved the thermal conductivity to 0.7 W/(m·K), representing a remarkable 133% increase compared to that of OA-TD alone. Even after undergoing 100 melting–solidification cycles, the OA-TD/EG-HDTMOS maintained superior phase-change thermal performance and stability, making it suitable for cold storage and energy conservation in air conditioning.

Strategizing Effective Maintenance Strategies for Air Conditioning Systems in Commercial Buildings in Malaysia: A Systematic Literature Review

The use of air conditioning systems is prevalent in many locations in Malaysia due to the elevated temperatures seen in the region. The importance of air conditioning system maintenance has not been accorded due attention. Taking this issue into consideration, this study aims to enhance the maintenance of air conditioning systems by understanding their importance, identifying maintenance issues and proposing solutions to enhance system maintenance, with a specific focus on commercial buildings in Malaysia. The data used in the study was gathered from case studies, images, newspapers, journals and previous theses. This study used fully qualitative methods to obtain data and was acquired through a systematic literature review (SLR) and open-ended interview questionnaire with experts. From the findings, it was found that there were three main strategies, which are preventive maintenance, corrective maintenance and predictive maintenance that are mostly applied for the maintenance of the air conditioning system at the commercial building in Malaysia. The overall findings from the interview with experts highlighted that technical expertise, specialized tools and comprehensive maintenance strategies were important elements to be employed by HVAC technicians and facility managers in Malaysia when managing the HVAC, especially the air conditioning system. This research expands knowledge by investigating air conditioning system maintenance. Furthermore, the findings of this study are useful to the maintenance team and users since they provide the best strategies for the maintenance of the air conditioning systems of commercial buildings in Malaysia.

Perancangan Tata Udara Menggunakan Metode Cooling Load Tempreture Difference (CLTD) Pada Studi Kasus Proyek X

This research is related to the design of air conditioning using the Cooling Load Temperature Difference (CLTD) method in the case study of Project X. One of the methods used is the Cooling Load Temperature Difference (CLTD) method. This method considers factors such as wet-bulb temperature, dry-bulb temperature, and air velocity to calculate the room cooling load. This research applies the CLTD method to a case study project in Jakarta with an air conditioning system in office buildings. This case study provides an example of the practical application of the CLTD method that is beneficial for practitioners in the field of air conditioning engineering. To implement the CLTD method, data from field studies such as room conditions, temperature, and humidity of the room and environment are needed. Next, calculations are made using the CLTD formula adjusted to the position of the sun in each room. The CLTD calculation results show that the highest sensible load is in the Boardroom, while the highest latent load is in the Conference Room. The design of the cooling system with the CLTD method shows optimal results and meets the requirements. The AC capacity calculated accurately and considering K4L factors results in an efficient, safe, and environmentally friendly cooling system.

Energy-saving diagnosis of public buildings based on multi-objective optimization algorithm

ABSTRACT This study focuses on existing ventilation, heating, and air conditioning systems in public buildings and mainly considers three objectives: system energy consumption, indoor thermal comfort, and efficiency. A multi-objective diagnosis system is established, which correlates evaluation indicators with key equipment, forming a clear hierarchical diagnosis system. Meanwhile, particle swarm optimization algorithm is combined with linear weighting method to optimize the operating parameters of key equipment based on the diagnosis results, obtaining the optimal parameters of various operating scenes. For winter conditions, the average system energy consumption is 34.2 W/m2, the average system energy efficiency ratio is 1.5, and the average indicator for indoor thermal environment is 1.06. For summer conditions, the average energy consumption of the systems is 28.9 W/m2, the average energy efficiency ratio is 2.2, and the average indicator for indoor thermal environment is – 0.82. Compared with the measured results, most of the optimized indicators are better, but the system energy efficiency ratio is slightly lower than the measured results for winter conditions. Through the established diagnosis system and optimization method, this research evaluates and optimizes the existing the systems in public buildings. Demonstrating the effectiveness of the established diagnosis system and optimization method.

Automatic control of constant temperature and humidity in building air conditioning systems based on frequency domain analysis

How to solve their automatic control of constant temperature and humidity gradually becomes a research hotspot as the continuous upgrading of air conditioning systems. This study aims to optimize the traditional proportional-integral-differential controller for improvement to solve the time-delay instability phenomenon in temperature and humidity control. The objective of this study is to optimize existing proportional-integral-differential controllers to improve the time-delay instability problem that is common in temperature and humidity control. Firstly, it treats the controlled object as a first-order and second-order system with time-delay characteristics. Next, the Smith predictor controller is generalized equivalent to ensure that the equivalent system does not contain time-delay. Finally, an analysis of the first-order and second-order closed-loop control system is conducted by combining Smith predictive controller and proportional-integral-differential controller. The system achieves the goal of automatic control of constant temperature and humidity by adjusting the control parameters. The experiment showcased that the temperature control time of the proposed control scheme under first-order and second-order time-delays was 16 s and 3 s, respectively. Meanwhile, the humidity control time was 14 s and 13 s, respectively. In practical applications, the proposed control scheme achieved good control effects in all four seasons. This indicates that the controller designed in this study possesses good control performance. It also can achieve the goal of constant temperature and humidity control. This can provide technical support for the automation control of air conditioning systems.

INTEGRATION OF MACHINE LEARNING FOR MICROCLIMATE MANAGEMENT OPTIMIZATION IN BUILDINGS: PERSPECTIVES AND OPPORTUNITIES

Modern machine learning (ML) technologies offer significant opportunities for optimizing microclimate management systems in buildings. In this article, we explore the potential application of ML methods for forecasting, adaptive control, and optimization of heating, ventilation, and air conditioning (HVAC) systems in buildings. We examine ML methods used for analyzing weather data, working hours, thermal needs, and user preferences to automatically optimize HVAC parameters. Additionally, we discuss the application of ML for detecting faults and preventing failures in microclimate systems, contributing to increased reliability and efficiency of building operations. Finally, we consider prospects for personalizing comfortable microclimates in buildings based on user preferences. Our analysis identifies the potential of ML for creating sustainable, energy-efficient, and comfortable buildings that meet modern requirements for microclimate management. Keywords: machine learning, microclimate management, HVAC Optimization, fault detection, predictive maintenance, user preferences, energy efficiency

Water quality constraints H2O2 production in a dual-fiber photocatalytic reactor

In-situ hydrogen peroxide (H2O2) finds applications in disinfection and oxidation processes. Photoproduction of H2O2 from water and oxygen, avoids reliance upon organic chemicals, and potentially enables smaller-sized or lower-cost reactors than electrochemical methods. In ultrapure water, we previously demonstrated a novel dual-fiber system coupling a light emitting diode (LED) with a metal-organic framework (MOF) catalyst-coated optical fiber (POF-MIL-101(Fe)) and O2-based hollow-membrane fibers and achieved a remarkable H2O2 yield, 308 ± 1.4 mM h−1 catalyst-g−1. To enable H2O2 production anywhere we sought to understand the impacts of common water quality parameters. The production of H2O2 was not affected by added sodium, potassium, hydroxide, sulfate or nitrate ions. There was consistent performance over a wide pH range (4–10), maintaining a high production rate of 232 ± 3.5 mM h−1 catalyst-g−1 even at pH 10, a condition typically unfavorable for H2O2 photoproduction. Chloride ions produced hypochlorous acid, consuming in-situ produced H2O2. Phosphate adsorption on the iron-based MOF catalysts blocked H2O2 production. Inorganic carbon species inhibited H2O2 production due to in-situ formic acid. Encouraging results were obtained using atmospheric water (i.e., condensate), with rates reaching 288 ± 6.1 mM h−1 catalyst-g−1, comparable to ultrapure water. This underscores atmospheric water as a variable alternative, available in nearly all building air conditioning systems or could overcome geographical constraints, particularly in regions where obtaining pure water resources is challenging, offering a cost-effective solution. The dual-fiber reactor using atmospheric water enables high-efficiency H2O2 production anytime and anywhere.

Applying OPTICS with and without PCA for fault detection of fan coil units using building automation system data

In building operations, Heating, Ventilation, and Air Conditioning (HVAC) system faults lead to substantial energy waste. Due to the rapid growth in the availability of sensing data and advancements in computational technology, AI-based methods have emerged as a powerful tool for Fault Detection and Diagnosis (FDD) of HVAC systems, significantly enhancing the efficiency of building operations. Several studies support supervised learning methods for FDD; however, these are limited in practice due to their reliance on labeled datasets, which are generally unavailable. In this context, unsupervised methods, notably those using cluster analysis, show great promise, particularly for analysing Building Automation System (BAS) data. This paper presents a methodology using OPTICS – an ordered clustering algorithm – for fault detection and explores the impact of PCA on its accuracy for fault detection, using both published and full-scale historical building data. While beneficial for reducing computational costs and enhancing noise reduction — thereby generally improving the clarity of cluster differentiation — PCA’s dimensionality reduction may result in the loss of critical information, leading to some clusters being less discernible or entirely undetected. These overlooked clusters could be indicative of underlying faults, and their obscurity represents a significant limitation of PCA when identifying potential faults in complex data.

Analysis of the technical and economic aspects of gas engine heat pumps in various climates in Iran

Due to the scarcity of fossil fuels and the potential for energy shortages, the use of energy-efficient devices is of utmost importance. There is an increasing trend in the utilization of heat pumps in air conditioning systems (heating/cooling) for both residential and commercial buildings. This article focuses on the analysis of the technical and economic aspects of gas engine heat pumps (GEHP) in various climates throughout Iran. The evaluation of this article revolves around two perspectives: that of the consumers and the government. The implementation of GEHP has been carefully considered from the consumers' perspective to ensure it does not have any negative effects on them. Additionally, from the government's point of view, the available resources are considered, and the supply of electricity and gas, along with their cost and energy consumption, are thoroughly investigated by the Ministries of Energy and Petroleum. The findings indicate that, from the consumers' perspective, the economic efficiency of GEHP can be up to 11 times higher than that of electric heat pumps (EHP). Moreover, from the government's viewpoint, GEHP leads to a reduction of over 92 % in electricity consumption, 5 % in gas consumption, and 92 % in annual expenses for the Ministry of Energy. Furthermore, the implementation of GEHP increases the income of the Ministry of Petroleum by 9.8 times.

COMPARATIVE EXERGY ANALYSIS OF ADIABATIC AND ISOTHERMAL HUMIDIFICATION IN A DIRECT-FLOW CENTRAL AIR CONDITIONING SYSTEM

Central air conditioning systems of buildings and structures are significant consumers of electric and thermal energy of different temperature levels and are the least economical in terms of energy consumption. When designing these systems, due attention is not always paid to assessing their energy efficiency and no variant design is carried out with a comparison of the relevant indicators. This is due to the lack of proven engineering methods. The exergy method of analysing energy-consuming systems provides a correct numerical assessment of energy efficiency and enables a comparative analysis of circuit solutions and system elements. In contrast to the energy method, the peculiarity of the exergy method in relation to air conditioning systems is the possibility of taking into account all available mass and energy flows in the system in exergy units. The extension of the exergy method to systems for creating and maintaining a microclimate in buildings and structures has not yet acquired the status of practical engineering support. This article presents an example of an exergy analysis based on the original methodology for comparing methods of humidification in a central air conditioning system for a public building in order to select the best method in terms of energy efficiency. Two traditional methods of air humidification for the cold season - adiabatic and isothermal - were used for the study and comparison. The results of analytical studies are presented in the form of relative values of exergy.