Building Air Conditioning Systems Research Articles

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).

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.

Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users

Reducing energy consumption is vital to save natural resources and contribute to the sustainable development in any sector of society. In the building sector, there are many well-known energy efficiency strategies currently being applied. However, considering the advances in technology and in comfort studies, it is possible to see that the current building sector scenario demands new energy efficiency strategies. Such strategies need to be capable of identifying and assuring comfortable environments according to users’ perceptions. Machine learning techniques can be a useful alternative to identify users’ preferences and control lighting and heating, ventilation and air-conditioning systems in buildings. This paper shows a systematic literature review on the use of machine learning algorithms on preference identification and environmental adequacy according to users’ demands. Its contribution is to explore beyond the performance and configurations of the algorithms, addressing users’ preference aspects as well. The strategies found in the literature provided promising results. The most used approach was supervised learning because data can be treated as categories. In general, the control systems have shown good performance, and so have the algorithms. Users were mostly satisfied with environmental conditions. Situations of dissatisfaction were associated with the occupant’s willingness to use the system more than with the control system’s performance. Furthermore, it is also possible to ally user-centred control and energy savings but this relies on occupants’ characteristics and the control strategies used. We underline the importance of identifying whether the users are willing to deal with an automatic control system before making any decision, even if the operation of the system is based on their preferred environmental conditions.

Unveiling overlooked aspects of model predictive control for building air conditioning systems

The ongoing research on model predictive control (MPC) for building air conditioning systems predominantly centers on improving the predictive capabilities of system models. In this paper, the impacts of three additional pivotal factors on MPC performance are assessed by examining a generic MPC design for a typical variable air volume (VAV) system that serves large commercial buildings. The three factors encompass the nuanced reformulation of optimization, the judicious relaxation of constraints, and the meticulous tuning of parameters. Detailed case studies with an integrated Modelica and EnergyPlus model of the US Department of Energy's Commercial Reference Building are conducted. The results confirm that the optimization formulation, along with relaxation methods, significantly affects MPC performance in terms of energy savings, zonal thermal comfort level, and computational demand. They also reveal that the impact of the MPC control parameters on the energy savings and thermal comfort may vary by season and can be non-monotonic.

Advancing energy efficiency in livestock building: Simplified building energy simulation tool for geometric design of pigsty

Concerns related to food security and animal welfare have led to increasing interest in analyzing the energy demands of heating, ventilation, and air conditioning (HVAC) systems in livestock buildings. However, building energy simulation (BES) model, which facilitates precise HVAC systems energy analysis, is difficult to use for non-experts, and particularly, its usability drops significantly in livestock buildings, which are often constructed without involvement of building engineers. This study, therefore, developed a tool that can analyze the energy demand of pigsties by changing geometric design variables with only several numerical inputs without separate 3D modeling. Pigsty geometric design types were classified by examining pigsty architectural design manual and web portal map search. A simplified tool, utilizing EnergyPlus and MATLAB graphical user interface, was developed to analyze energy demand in pigsties by leveraging simple user inputs for geometric design modeling. This tool simplifies exploring potential energy savings through geometric design changes in the early design stages of livestock buildings. The methodology introduced in this study lays the groundwork for future enhancements, including the integration of various design-phase variables beyond geometric design.

Reconfigurable supply-based feedback control for enhanced energy flexibility of air-conditioning systems facilitating grid-interactive buildings

Air-conditioning systems have great potential to provide energy flexibility services to the power grids of high-renewable penetration, due to their high power consumption and inherent energy flexibilities. Direct load control by switching off some operating chillers is the simplest and effective means for air-conditioning systems in buildings to respond to urgent power reduction requests of power grids. However, the implementation of this approach in today's buildings, which widely adopt demand-based feedback controls, would result in serious problems including disordered cooling distribution and likely extra energy consumption. This study, therefore, proposes a reconfigurable control strategy to address these problems. This strategy consists of supply-based feedback control, incorporated with the conventional demand-based feedback control, a control loop reconfiguration scheme and a setpoint reset scheme, facilitating effective control under limited cooling supply and smooth transition between supply-based and demand-based feedback control modes. The proposed control strategy is deployed in a commonly-used digital controller to conduct hardware-in-the-loop control tests on an air-conditioning system involving six AHUs. Test results show that the reconfigurable control achieves commendable control performance. Proper chilled water distribution enables even thermal comfort control among the building zones during demand response and rebound periods. Temperature deviation of the building zones is controlled below 0.2 K most of the time. 11.6 % and 27 % of power demand reductions are achieved during demand response and rebound periods respectively, using the proposed reconfigurable control compared with that using conventional controls.