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As new energy integration increases, power grid load curves become steeper. Large logistics parks, with their substantial cooling load, show great peak shaving potential. Leveraging this load while maintaining staff comfort, product quality, and operational costs is a major challenge. This paper proposes a two-stage robust optimization method for large logistics parks to participate in grid peak shaving. First, a Cooling Load's Economic Contribution (CLEC) index is introduced, integrating the Predicted Mean Vote (PMV) and Sales Pressure Index (SPI). Then, an optimization model is established, accounting for renewable energy uncertainties and maximizing large logistics parks' participation in peak shaving. Results illustrate that the proposed method leads to a reduction in the peak shaving pressure on the distribution network. Specifically, under the scenario tolerating the maximum potential uncertainty in renewable energy output, the absolute peak-to-valley difference and fluctuation variance of the park's net load are decreased by 45.82% and 54.59%, respectively. Furthermore, the PMV and the SPI indexes are reduced by 39.12% and 26.36%, respectively. In comparison with the determined optimization method, despite a slight cost increase of 20.06%, the proposed method significantly reduces EDR load shedding by 98.1%. [ABSTRACT FROM AUTHOR]

Addressing the challenges of scalable and cost-effective energy performance analysis in mid to high-rise office buildings, this paper introduces a novel approach utilizing an inverse-based artificial neural network (ANN). This ANN was trained on synthetically generated heating and cooling load parameters – derived from simulations conducted in EnergyPlus – to predict characterization parameters, including the building envelope, internal heat gains, and HVAC operational parameters. Diverging from traditional forward surrogate models, this inverse surrogate model fills a critical gap in current building energy modeling approaches that are hindered by data and resource limitations. Its effectiveness is verified with a testing dataset of 3000 buildings and is further demonstrated through a case study in Ottawa, Ontario. Proving to be an efficient, cost-effective tool for energy retrofit screening, the model is enhanced by a user-friendly web-based application (Ferreira and Gunay), marking a significant advancement in accessible building energy analysis. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to achieve lower and lower temperature as infrared sensors works faster and better used for space application. For getting good quality images from space, the infrared sensors are need to keep in cryogenic temperature. Cooling to cryogenic temperatures is necessary for space-borne sensors used for space applications. Infrared sensors work faster or better at lower temperatures. It is the need for time to achieve lower and lower temperatures. Design/methodology/approach: This study presents the investigation of the critical Stirling cryocooler parameters that influence the cold end temperature. In the paper, the design approach, the dimensions gained through thermal analysis, experimental procedure and testing results are discussed. Findings: The effect of parameters such as multilayer insulation, helium gas charging pressure, compressor input voltage and cooling load was investigated. The performance of gold-plated and aluminized multilayer insulation is checked. The tests were done with multilayer insulation covering inside and outside the Perspex cover. Practical implications: By using aluminized multilayer insulation inside and outside the Perspex cover, the improvement of 16 K in cool-down temperature was achieved. The cryocooler is charged with helium gas. The pressure varies between 14 and 18 bar. The optimum cooling is obtained for 17 bar gas pressure. The piston stroke increased as the compressor voltage increased, resulting in total helium gas compression. The optimum cool-down temperature was attained at 85 V. Originality/value: The cryocooler is designed to achieve the cool-down temperature of 2 W cooling load at 100 K. The lowest cool-down temperature recorded was 105 K at a 2 W cooling load. Multilayer insulation is the major item that keeps the thermal radiation from the sun from reaching the copper tip. [ABSTRACT FROM AUTHOR]

Multi-layered walls are commonly used building elements that have the potential to reduce cooling loads by improving thermal insulation. This paper investigates the potential of reducing cooling load using different types of multi-layered. For this purpose, a model of a small room (1.15 × 1 × 1 m) was constructed. A software code based on the radiant time series method was developed using MATLAB to extract heat gain results. The results were verified with other researchers, and there was a 0.89% error. Overall, the results show that using solid or hollow bricks in construction can be an effective way to reduce heat gain, where Wall-C achieved the minimum heat gain of 60.8 W m−2 compared to 66.207 and 71.225 W m−2 for Wall-A and Wall-B respectively. The reasons for this could be due to the insulation hollow area provided by the bricks, which tends to reduce heat transfer through the wall. [ABSTRACT FROM AUTHOR]

Transformer efficiency is a key concern for both transformer operators and regulators, particularly with regards to new transformers. Cooling is a critical factor in preventing overheating and controlling the ageing of transformer insulation. This paper aims to analyse how different cooling setups can influence efficiency. Transformer losses depend on the winding temperature, which is affected by load, ambient temperature, and cooling power. In this study, we will take all of these factors into consideration to examine their impact on transformer losses. Using a 520 MVA transformer as an example, we will calculate the loss levels under different realistic ambient temperature and load profiles. We will also apply different cooling system settings to determine the optimization possibilities. Our calculations will consider different turn-on temperatures of the fans and cooling stages and will use steady state calculations over a period of 12 months. Additionally, a novel approach to control cooling by using the loading of the transformer is present, as are the possibilities of using variable speed fans. Finally, we will estimate the influence of cooling settings on transformer ageing by examining their impact on temperature. [ABSTRACT FROM AUTHOR]

This paper investigated the effect of individual differences and the type of heating system on the perceived thermal comfort condition, neutral temperature, and thermal sensitivity in cold outdoor air conditions. Three types of individual factors, such as gender, temperament, and thermal past experience, were studied and compared in this paper. According to the results, temperament and thermal past experience directly influenced the occupants' thermal preference and indirectly influenced the thermal sensation by affecting their clothing level. However, the effect size of such variations was negligible. The type of heating system had the most significant impact on the neutral temperature, with a mean difference of 2.8°C. Individual factors did not have a direct impact on the neutral temperature. However, thermal sensitivity was influenced by an individual's temperament. Occupants with a cold temperament were more sensitive to the variation in the running mean outdoor temperature (Trm) than those with a warm temperament. When the results were compared with the predictions by the adaptive thermal heat balance (ATHB) and the predicted mean vote (PMV) models, it indicated that if the control condition over the heating system became limited, there would be no (i.e., very low) behavioral, physiological, and psychological adaptation. However, by creating an opportunity to control the heating system, thermal adaptation could be reached at a lower temperature under cold outdoor air conditions. Comfort is subjective and influenced by environmental factors and personal differences. For example, individuals might feel different and hotter or cooler at the same ambient temperatures. In addition, individuals could adapt to the environment and afford their comfort with lower costs than the heat predicted mean vote (PMV) and the cooling load calculation showed. The results of this paper could help to understand why people have different thermal perceptions under the same environment and might lead to the identification of the important nonenvironmental factors that impact the perceived feeling of health, well-being, and educational performance. Therefore, influencing decision-making during the design and maintenance of educational buildings. The results indicated that individual differences influenced the level of clothing and occupant preference. These differences influenced the variation in occupants' thermal sensitivity to indoor and outdoor air temperatures. However, they had a negligible influence on the neutral temperature. The heating systems had the most impact on the neutral temperature. The opportunity to control heating systems could lead to the thermal comfort condition being achieved at a lower temperature in cold outdoor air temperatures, which could reduce energy consumption. [ABSTRACT FROM AUTHOR]

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R² (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R² value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R² (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings' cooling load. [ABSTRACT FROM AUTHOR]

In this paper, a heat recovery ground source heat pump (HR-GSHP) system, in which the primary pipes of the GSHP for air conditioning and the GSHP for hot water are connected to ground heat exchangers (GHEs) and each GSHP is operated simultaneously or within a short period of time, was installed in a dormitory building on a trial basis. Then, the optimal operation method to minimize the energy consumption of the system was investigated. The operating period of the GSHP for HW was changed and simulations were conducted to determine the operating period with the lowest energy consumption, which was 8 months from April to November. Furthermore, the HR-GSHP system was operated for 8 years from 2012 to 2019, and actual measurements were carried out to verify the system performance and the energy saving effect in optimal operation. In actual operation, it was confirmed that the minimum temperature was about 10 °C or higher even when the GSHP for HW was operated year-round. Therefore, the GSHP for HW was operated year-round after the third year of operation. It was confirmed that the operation of the GSHP for HW in summer, especially in August and September when the cooling load is large, can improve the system's efficiency by the effect of recovering cooling exhaust heat. In the eighth year of operation, when the GSHP for HW was operated most during the summer season, the system was able to reduce power consumption for air conditioning and hot water supply by approximately 17%. [ABSTRACT FROM AUTHOR]

This paper presents the energy, exergy, and economic analysis of a new cogeneration cycle for the simultaneous production of power and cooling operating with an ammonia–water mixture. The proposed system consists of an absorption cooling system integrating a reheater, a separation tank, a compressor, a turbine, and an expansion valve. In addition, internal rectification is applied, improving the system's performance. Mass, energy, and exergy balances were applied to each system's component to evaluate its performance. Additionally, the costs of each component were determined based on economic equations, which take into account mass, heat flows, and temperature differences. A parametric analysis found that the system reached an energy utilization factor of 0.58 and an exergy efficiency of 0.26 using internal rectification at TG = 120 °C, TA = 30 °C, and TE = 10 °C. The power produced by the turbine was 26.28 kW, and the cooling load was 366.8 kW. The output costs were estimated at 0.071 $/kW. The condenser was found to be the most expensive component of the system, contributing 28% of the total cost. On the other hand, it was observed that the generator was the component with the highest exergy destruction, with 38.16 kW. [ABSTRACT FROM AUTHOR]

Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA- MLPRegressor performed well in terms of different metrices variables and fitted time. [ABSTRACT FROM AUTHOR]

With the increase in energy demand and carbon emission requirements, energy management and CO2 emission reduction on the user side are significant for integrated energy system (IES) scheduling. This paper constructs a two-stage optimal dispatching model combining active/passive demand response (DR) and reward and punishment ladder-type carbon trading. In the first stage, the electricity/gas load was optimized by adding a logistics function to the active DR model. A passive DR model is built based on the optimized electric load and the heat/cooling–electricity ratio of the cogeneration unit to optimize the heat/cooling load. In the second stage, a multi-objective IES model based on the optimized load and reward and punishment ladder carbon trading was solved by the golden eagle multi-objective optimization (MOGEO). Analyzing the scheduling results from different scenarios, the active/passive DR model can reduce the load peak-to-valley difference and improve user satisfaction and grid revenue. At the same time, the combination of reward and punishment ladder carbon trading makes the system gain more carbon revenue, reduces system operating costs and carbon emissions, and improves energy utilization. [ABSTRACT FROM AUTHOR]

During the design and planning stage of a district energy system, the prediction of the cooling and heating loads is an important step. The accurate estimate of the load pattern can provide a basis for the configuration and optimization of the system. To meet the demand in practical application, this paper proposes a fast load prediction method for district energy systems based on a presimulated forward modelling database and KNN (K-nearest neighbor) algorithm and develops it into a practical tool. Owing to the absence of some design parameters at the planning stage, scenario analysis is also used to determine some input conditions for load prediction. In this paper, the scenarios cover three types of building: office, shopping mall and hotel. To test the performance of this new method, we randomly selected 15 virtual buildings (5 buildings for each type) with different design parameters and took their detailed BPS (building performance simulation) model as a benchmark to assess the prediction results of the new method. The index "ratio of the hours with effective prediction" is defined as the ratio of the hours whose relative error of hourly load prediction is less than 15% to the hours whose load is not 0 in the whole year, and the test result shows that this index is not less than 0.9 (90%) for the predicted cooling load of all 45 test cases and the predicted heating load of 25 of the 45 cases. As a research achievement with practical value, this paper accomplishes the programming work of the tool and makes it into a software. The application of this software in the actual project of district energy system is also presented. The performance of the new load prediction tool was compared with the traditional approach commonly used in engineering—the load estimation based on reference building models—and the result shows that the fast load estimate tool can provide the same level of prediction accuracy as traditional simulation methods. [ABSTRACT FROM AUTHOR]

Superconducting electric motors are required in order to deliver lower-carbon aviation. Critical to the success and viability of operating superconducting electric motors in aviation is keeping the superconducting coils at their operating temperature. This paper examines the challenges of keeping a superconducting motor cold if it were used on a single aisle passenger aircraft such as an Airbus A320. The cooling problem is defined and different cooling scenarios are investigated to determine viability. The investigation has shown that for a motor with a superconducting rotor only (copper stator), a Stirling-type cryocooler would be sufficient. However, if the motor is to be fully superconducting, then the cooling loads of the stator, which are much higher, make mechanical refrigeration impractical and the only option is to cool the motor with the heat sink of a liquid hydrogen fuel. [ABSTRACT FROM AUTHOR]

Commercial buildings in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This paper aims to address this gap by investigating the application of innovative deep-learning models in day-ahead hourly cooling load prediction for commercial buildings in tropical climates. A range of multi-output deep learning techniques, including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs), are employed to enhance prediction accuracy. Furthermore, these individual deep learning techniques are synergistically integrated to create hybrid models, such as CNN-LSTM and Sequence-to-Sequence models. Experiments are conducted to choose the time horizons from the past that can serve as input to the models. In addition, the influence of various categories of input parameters on prediction performance has been assessed. Historical cooling load, calendar features, and outdoor weather parameters are found in decreasing order of influence on prediction accuracy. This research focuses on buildings located in Singapore and presents a comprehensive case study to validate the proposed models and methodologies. The sequence-to-sequence model provided better performance than all the other models. It offered a CV-RMSE of 7.4%, 10%, and 6% for SIT@Dover, SIT@NYP, and the simulated datasets, which were 2.3%, 3%, and 1% less, respectively, than the base Deep Neural Network model. [ABSTRACT FROM AUTHOR]

Many researchers are devoted to improving the prediction accuracy of daily load profiles, so as to optimize day-ahead operation strategies to achieve the most efficient operation of district heating and cooling (DHC) systems; however, studies on load prediction and operation strategy optimization are generally isolated, which leaves the following question: what day-head load prediction performance should be paid attention to in the operation optimization of DHC systems? In order to explain this issue, and taking an actual DHC system as a case study, this paper proposes an evaluation method for the prediction of daily cooling load profiles by considering the impact of inaccurate prediction on the operation of a DHC system. The evaluation results show the following: (1) When prediction models for daily load profiles are developed, the prediction accuracy of the daily mean load should be emphasized, and there is no need to painstakingly increase the accuracy of load profile shapes. (2) CV and RMSE are the most suitable deviation measures (compared to others, e.g., MAPE, MAE, etc.) for the evaluation of load prediction models. A prediction model with 27.8% deviation (CV) only causes a 3.74% deviation in operation costs; thus, the prediction performance is enough to meet the engineering requirements for the DHC system in this paper. [ABSTRACT FROM AUTHOR]

This paper studies the combined heating and cooling thermal performance of a CO2 heat pump system considering the subcooling effect. For such a system without cold thermal energy storage (CTES), the gas cooler outlet temperature normally needs to be controlled to match the cooling load required. However, the integration of CTES would enable the system to be operated under its optimal conditions depending on the ambient temperatures, i.e. a considerable amount of cooling capacity can be generated and stored for later use. A configuration of a CO2 heat pump integrated with CTES is described in this paper. A thermodynamic cycle and a simulation model considering the subcooling effect have been developed. The mathematical model for the pinch point analysis has been newly validated against published experimental data with acceptable agreements. In the case study, the impacts of the subcooling temperature on the optimal combined performance under four ambient temperatures (5 °C, 15 °C, 25 °C, and 32 °C) have been studied. The highest optimal combined COP of 5.38 can be achieved when the ambient temperature is 5 °C. The detailed profiles of CO2 temperatures, heating and cooling loads, and the COPs when the CTES is in operation have been revealed for the first time. It is found when the ambient temperature is higher than the water inlet temperature (plus the pinch point temperature), the optimal cooling COPs can even have a surge without the subcooling effect, due to a lower sCO2 temperature leaving the heat exchanger compared to the ambient temperature. Additionally, performance analysis for the CO2 heat pump system with or without CTES is compared, and it is concluded that all optimal heating, cooling, and combined COPs integrated with CTES surpass those without CTES. [ABSTRACT FROM AUTHOR]

This paper presents a detailed study of the energy savings that can be achieved through residential appliances by implementing conservation voltage reduction (CVR). The computed energy savings are derived from the ZIP coefficients of a load model (static) under varying voltage conditions that represent the power consumed by each appliance tested. Multiple studies have shown energy savings at the substation level but not at the level of a specific residence and appliance; hence, the latter is the focus of this analysis. The appliances are determined based on the type of heating and cooling loads, and the daily duration of use for each appliance, which contributes to the total monthly consumption that is billed for a household are included in this study. Energy savings in Malaysia has been a focus of many current studies, and this study is the first attempt to achieve energy savings through the implementation of CVR. No published work has compared individual appliances that truly benefits from CVR, and this is addressed in this study. The main contribution of this study is that it provides very detailed and measured data that are used to analyze and generate energy savings for very specific residential appliances intended to meet every 1% voltage drop. The load model is developed for the common household appliances in Malaysia, and then a voltage reduction study is applied to the appliances as well as the Malaysian distribution networks. Here, we also provide insight for performing voltage reduction at an electric vehicle (EV) charging station in Malaysia. The results of this study should serve as a foundation for all practicing utilities engineers to address the issue of CVR in Malaysia. The CVR factors for residential, commercial, and industrial networks in Malaysia are also published in this paper. [ABSTRACT FROM AUTHOR]

The climate data used for dynamic energy simulation of buildings located in urban regions are usually collected in meteorological stations situated in rural areas, which do not accurately represent the urban microclimate (e.g., urban heat island effect), and this might affect the simulation accuracy. This paper aims at quantitatively evaluating the effects of heat island on a high-rise building's energy performance based on the microclimate simulation tool ENVI-met and the building energy simulation tool COMFIE. However, the computation of microclimate models is time consuming; it is not possible to simulate every day of a year in a reasonable time. This paper proposes a method that generates hourly "site-specific climate data" to avoid long microclimate simulation times. A coupling method of ENVI-met and COMFIE was developed for more precise building energy simulation, accounting for the heat island effect. It was applied to a high-rise building in Wuhan, China. The results showed that the yearly average urban heat island effect intensity at the height of 3 m was estimated to be 0.55 °C and decreased with height. Compared to the simulation considering the outdoor temperature variation with the height and orientation, using the original climate data collected in rural areas led to an overestimation of the heating load by around 5.8% and an underestimation of the cooling load by around 8.7%. Compared to the weather file at the height of 3 m near the north facade neglecting the temperature variation along the height, the heating load was overestimated by 8.2% and the cooling load was underestimated by 10.8%. The methods proposed in this paper can be used for the more precise application of urban building energy simulation. [ABSTRACT FROM AUTHOR]

Learning an optimal control strategy from the optimized operating dataset is a feasible way to improve the operational efficiency of HVAC systems. The operation dataset is the key to ensuring the global optimality and universality of the operation strategy. Currently, the model-based method is commonly used to generate datasets that cover all operating scenarios throughout the cooling season. However, thousands of iterative optimizations of the model also lead to high computational costs. Therefore, this paper proposed a scenario reduction method in which similar operating scenarios were grouped into clusters to significantly reduce the number of optimization calculations. First, k-means clustering (with dry-bulb temperature, wet-bulb temperature, and cooling load as features) was used to select typical scenarios from operating scenarios for the entire cooling season. Second, the model-based optimization was performed with the typical scenarios to generate the optimal operating dataset. Taking a railway station in Beijing as a case study, the results show that the optimization time for the typical scenarios was only 1.4 days, which was reduced by 93.1% compared with the 20.6 days required to optimize the complete cooling season scenario. The optimal control rules were extracted, respectively, from the above datasets generated under the two schemes, and the results show that the deviation of energy saving rate was only 0.45%. This study shows that the scenario reduction method can significantly speed up the generation of the optimal control strategy dataset while ensuring the energy-saving effect. [ABSTRACT FROM AUTHOR]

There is currently no way for subway air-conditioning systems to adjust to changes in passenger capacity in terms of air-supply volume and air-supply temperature. In this paper, a study of the setting of the air-supply volume and controlling the temperature in subway trains was conducted. Based on the fact that the air-supply volume in a subway train needs to meet the requirements of cooling load and humidity load, as well as fresh-air volume, the minimum critical air-supply volume curve was determined on the basis of theoretical calculations under different passenger capacities. Under different passenger capacity and air-supply-volume conditions, the PMV (predicted mean vote) distribution of the subway train was simulated and calculated, and the results indicated that increasing the air-supply volume would not be sufficient to achieve PMV = 0. Therefore, in accordance with the theoretical formulation of the PMV, multiple linear regression was used to fit the temperature-control curve for various passenger capacities. A simulation calculation of the subway train was used to assess the uniformity of the flow field and the temperature field within the subway train in accordance with EN14750. It is evident from the results that under the control curve of the air-supply volume and temperature under different passenger capacities, the uniformity of the flow field and the temperature field can meet the standard requirements. By using the air-supply volume and temperature-control curve proposed in this paper, it is possible to adjust the air-supply volume in real-time, control the temperature of the air-conditioning system of the subway train, improve the passenger comfort of the train, and reduce the energy consumption of the air-conditioning system. [ABSTRACT FROM AUTHOR]

South African universities boast a remarkable solar photovoltaic (PV) resource as a primary renewable energy component. Due to high peak demand tariffs and inherent prominent heating and cooling loads, fast and granular demand response programs are well established within typical campus grids, with electrical networks adapted towards hosting centralized PV plants and emergency diesel generation. With unreliable utility supply and aging infrastructure comes a natural landscape and niche application for campus microgrids (MG) in South Africa. One such case, the University of the Free State's QwaQwa satellite campus in the Phuthaditjhaba district, is no exception to this, as it has sufficient solar PV generation, but it also has an unreliable utility component. This paper investigates a possible MG for the UFS QwaQwa campus with an emphasis on Hybrid PV-Diesel dispatch strategies, specifically, to ensure uptime during the loss of grid supply and decrease fuel usage. The proposed centralized diesel-PV MG system achieves a diesel cost reduction of 21.55%, based on simulated results using actual campus load data from 2019. The approach improves electricity availability, supplying 100% of all campus demand, compared to 70% under a de-centralized approach. [ABSTRACT FROM AUTHOR]

A major portion of the worldwide emissions arise from mobile air-conditioning systems with hydrofluorocarbon refrigerant as working substance and which is one of major cause for the greenhouse effect. R134a refrigerant having GWP of 1400 has been extensively used in car air conditioning. To reduce greenhouse gas emissions, the current R134a refrigerant must be phase out as per Kigali Amendment. The present study deals with cooling load calculation of car model by heat balance method as per ASHRAE standard using local climate condition. Further, thermodynamic analysis of R1234yf as an alternate refrigerant to R134a has been carried out for automobile air conditioning system. The required properties of refrigerants are extracted from Engineering Equation Software. The thermodynamic analysis is carried out to study the effect of operating parameters viz. condensing temperature, evaporating temperature, degree of superheating and degree of subcooling on COP, EDR, exergy efficiency and entropy generation. The previous literature reports mainly focus on separate study of either cooling load calculation or energy analysis or exergy analysis of R1234yf and R134a for automobile air conditioning system, while this paper presents the comprehensive study of new low GWP R1234yf as an alternate refrigerant to R134a in automobile air conditioning system with cooling load calculation including the concept of energy, entropy and exergy analysis. The percentage difference in COP between R134a and R1234yf system varies from 2.44 % to 4.78 % while percentage difference in EDR varies from 6.79 % to 2.87 % when evaporating temperature varied from -10 °C to 10 °C. With 12 °C of superheating at compressor inlet, the COP of R134a is 3.9 whereas COP of R1234yf is 3.75, which makes 3.85 % lower than that of R134a. The R1234yf has 4.78 % lower value of exergy efficiency as compared to that of R134a at evaporating temperature of -10 °C and it is found that maximum exergy destruction takes place in compressor. [ABSTRACT FROM AUTHOR]

To fairly use demand response to regulate customer load, support the economic and environmental protection, and assess the quantity and quality of the synergistic growth of the integrated energy system, a multi-objective optimum scheduling model and a solution method considering exergy efficiency and demand response are presented. To begin with, a mathematical model of each energy gadget is created. The electricity-gas load demand response model is then built using the price elasticity matrix, while the cooling load demand response model is built taking into account the user's comfort temperature. On this basis, a multi-objective optimal dispatching model is developed with the optimization goals of minimizing system operation costs, reducing carbon emissions, and increasing exergy efficiency. Finally, the model is solved using NSGA-II to produce the Pareto optimal frontier solution set in various situations, and the VIKOR decision procedure is utilized to identify the complete best dispatching solution. The simulation results suggest that the proposed model can match the system's scheduling needs in terms of numerous objectives such as economy, environmental protection, and exergy efficiency while also assuring user's comfort. [ABSTRACT FROM AUTHOR]

This paper proposes a modified golden jackal optimization (IGJO) algorithm to solve the OCL (which stands for optimal cooling load) problem to minimize energy consumption. In this algorithm, many tools have been developed, such as numerical visualization, local field method, competitive selection method, and iterative strategy. The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed. In order to fully utilize the effectiveness of the proposed algorithm, three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works. The results show that the IGJO algorithm can find solutions equal to or better than other methods. Underpinning these studies is the need to reduce energy consumption in air conditioning systems, which is a critical business and environmental decision. The Optimal Chiller Load (OCL) problem is well-known in the industry. It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling. In order to solve the OCL problem, an improved Golden Jackal optimization algorithm (IGJO) was proposed. The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed. These studies are intended to address more effectively the issue of OCL, which results in energy savings in air-conditioning systems. The performance of the proposed IGJO algorithm is evaluated, and the results are compared with the results of three known OCL problems in the ventilation system. The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air. [ABSTRACT FROM AUTHOR]

This paper evaluated the cement-based materials incorporated with novel solid–gel phase change materials (PCMs) for improving the energy efficiency of building envelopes. This novel PCM is form-stable, which will not leak as solid–liquid PCMs do and not need encapsulation, and it features high energy-storage capacity. Experimental results showed that the thermal properties of cement-based materials were improved as the increase of PCM content. A 30% replacement of sand by volume with PCM can increase the latent heat of the mixture from around 0 to 7 J/g and decrease the thermal conductivity of PCM mortar based on the generalized self-consistent (GSC) model by about 20%. However, the workability and mechanical properties were compromised. The simulation results indicated that 30% PCM-incorporated walls can contribute to 5% energy saving for cooling in a whole year and 12% reduction in peak cooling load compared with the reference without PCM. The proposed PCM composite offers a promising avenue to achieve energy-efficient building envelopes. [ABSTRACT FROM AUTHOR]

When investigating supermarket refrigeration systems, it is essential to consider the refrigeration load as it directly affects the required compressor capacity and energy consumption. Therefore, it is important to have knowledge of the refrigeration load and its main influencing factors. Direct usage of data for the refrigeration load in a whole-year energy simulation can present significant challenges. This is partly due to long lead times for data collection and the challenges posed by missing data. As a result, often there is no complete data set of the refrigeration load for the whole-year. Therefore, a mathematical model can serve as a tool to interpolate and extrapolate the refrigeration load over the course of a year. The model is not intended for transfer to other locations. This paper begins with a overview of correlations found in the literature, followed by the development of an improved model that accounts for the impact of ambient temperature and time on the refrigeration load. Additionally, a novel approach using a neural network is proposed as a second model. Both models are then applied to data from three supermarkets in Europe and the model performance is evaluated. Both models performed acceptably in representing the refrigeration load of all three supermarkets. Especially including the influence of time of day into the first model significantly improves the prediction accuracy. The neural network has the highest prediction performance and can improve the prediction performance by 40% over the improved literature model. [ABSTRACT FROM AUTHOR]

Hydrogen is characterized by zero carbon emissions and high energy density, which can effectively support the consumption of a high proportion of intermittent new energy. Considering the seasonal nature of renewable energy sources, a seasonal hydrogen storage model is incorporated in an electric-hydrogen integrated energy system (EH-IES). In this paper, a two-layer optimization method is proposed for EH-IES with seasonal hydrogen storage. The problem of co-optimizing the equipment capacity and configuration in the proposed system is coordinated by establishing a two-layer optimization framework. Specifically, the system is optimized to minimize cost and carbon emissions at the upper layer using the multi-objective stochastic paint optimizer (MOSPO) algorithm, with the capacity configuration results being transmitted to the lower layer. The lower layer, aiming to reduce the total system cost, utilizes a commercial solver to obtain the optimal economic scheduling results for a typical day. The final analysis of the four scenarios shows that the increase in renewable energy reduces the purchasing cost of electricity by 1.89%, while in contrast, the total cost increases by 4.4% in the system with a lower proportion of renewable energy. In the case of higher heating and cooling loads, the increase in renewables reduces the purchase cost of natural gas by 9.10%. The results demonstrate that the proposed method can leverage the seasonal complementary benefits to drive new energy consumption, enhance system operation efficiency, and effectively reduce EH-IES's total operation cost and carbon emission. [Display omitted] • Seasonal hydrogen storage is introduced into the electric-hydrogen integrated energy system model. • A two-layer optimization model is developed in the electric-hydrogen integrated energy system. • A Multi-objective Stochastic Paint Optimizer (MOSPO) method is employed to solve the planning problem. [ABSTRACT FROM AUTHOR]

Generally, the central air conditioners are centralized in a refrigeration room in public buildings. The proportion of energy consumption of the refrigeration room is about 40% of entire building. This paper aims to propose an energy plus and self-designed simulator to reduce the energy consumption of a large electronic factory in Zhuhai City, Guangdong province, China. In this paper, the real data collected from the electronic factory in February and the group control method used in the refrigeration room based on the kernel ridge regression algorithm. The correctness of the kernel ridge regression algorithm was verified with the experimental results. Compared to traditional PID control method, the kernel ridge regression algorithm could dynamically adjust those parameters, such as temperature and frequency to match the high efficiency zone of each device. The overall average COP of the system is increased from 4.17 to 7.04. The electricity of the refrigeration room could save 64 MWh with the cooling load forecast error being below 7% in February. The power consumption is expected to reduce by 8.2% and 562.3 MWh in 2022. [ABSTRACT FROM AUTHOR]

A ceiling cooling system integrated with a mechanical ventilation system has been widely used in modern buildings with large sensible cooling loads due to the high thermal comfort level and large energy efficiency. However, there is a lack of systematic research on the influence factors such as ceiling surface temperature and cooling load on the indoor air distribution and thermal environment, and the impact of ventilation system type in the ceiling cooling room is still unclear. Therefore, this paper presented an experimental study of indoor air distribution and thermal environment in a ceiling cooling (CC) room with mixing ventilation (MV), underfloor air distribution (UFAD) and stratum ventilation (SV); the ceiling surface temperature was 17 °C–26 °C and the internal or external cooling load was 41.5 W/m2–69.5 W/m2. The results showed that the vertical air temperature difference and contaminant removal effectiveness were 0.2 °C–0.4 °C and 0.53–0.85 with CC + MV, 0 °C–1.2 °C and 0.68–1.25 with CC + UFAD and 0.3 °C–0.9 °C and 0.50–0.83 with CC + SV, and the corresponding heat removal effectiveness and air diffusion performance index were 0.96–1.11 and 96–100%, 0.9–1.5 and 57–100% and 1.11–1.34 and 71–100%, respectively. Moreover, the difference between mean radiant temperature and air temperature and the predicted mean vote of thermal sensation were from 0 °C to 0.9 °C and between 0 and 0.26 with CC + MV, from −0.1 °C to 2.2 °C and between −0.1 and 0.42 with CC + UFAD and from −0.1 °C to 0.9 °C and between −0.2 and 0.13 with CC + SV. Hence, the ventilation system type clearly affected the indoor air distribution and thermal environment in the ceiling cooling room, and the experimental results would be beneficial for the design and control of a ceiling cooling system combined with a mechanical ventilation system in practice. [ABSTRACT FROM AUTHOR]

This paper presents experimental and thermodynamic investigations of the performance of commercial refrigeration systems with capacities varying from 6 to 26 tons of refrigeration. The analysis considers a critical comparison between the conventional and widely used single-phase approach with the actual multi-phase methodology. The tests were performed under practical conditions with state-of-the-art experimental setups using variable cooling loads. The tests were performed in a climate chamber with advanced ambient temperature and humidity control equipment, where temperature can be raised up to 65 °C, and humidity can vary from 10 to 90%. This enabled us to simulate a wide range of operating modes to validate the performances of the proposed two-phase refrigeration system with theoretical results obtained from single-phase modeling. It was also found that the refrigeration system with a larger cooling capacity exhibited an operational performance close to two-phase theoretical results more than the small refrigeration system capacity. The experimental results were found to comply with the theoretical results with an error of approximately 10% for the two-phase approach compared to an error of approximately 20% for the single-phase approach. [ABSTRACT FROM AUTHOR]

In this paper, an experimental investigation of the performance improvement of a heat pump equipped with a two-phase ejector, called an "ejector–expansion heat pump (EEHP)", is proposed. The system performance of the EEHP is compared with that of a vapor-compression heat pump (VCHP). The improvement potential is determined and discussed. The heat pump test system based on a water-to-water heat pump that can experiment with both the EEHP and the VCHP is constructed. A two-phase ejector with a cooling load of up to 2500 W is installed for the experiment. The results show that the EEHP always produces a higher heating rate and COPHP than the VCHP under the specified working conditions. The heating COPHP is increased by 5.7–11.6% depending on the working conditions. It is also found that, under the same heat sink and heat source temperature, the EEHP can produce a lower compressor discharge temperature and a lower compressor pressure ratio than the VCHP. This is evidence that the two-phase ejector can provide the compressor with better working characteristics, which yields a longer compressor lifetime. It is demonstrated that the expansion pressure ratio is key to the performance of the EEHP. A larger expansion pressure ratio yields greater improvement potential when compared with the VCHP. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to target on designing a short-term load prediction framework that can accurately predict the cooling load of office buildings. Design/methodology/approach: A feature selection scheme and stacking ensemble model to fulfill cooling load prediction task was proposed. Firstly, the abnormal data were identified by the data density estimation algorithm. Secondly, the crucial input features were clarified from three aspects (i.e. historical load information, time information and meteorological information). Thirdly, the stacking ensemble model combined long short-term memory network and light gradient boosting machine was utilized to predict the cooling load. Finally, the proposed framework performances by predicting cooling load of office buildings were verified with indicators. Findings: The identified input features can improve the prediction performance. The prediction accuracy of the proposed model is preferable to the existing ones. The stacking ensemble model is robust to weather forecasting errors. Originality/value: The stacking ensemble model was used to fulfill cooling load prediction task which can overcome the shortcomings of deep learning models. The input features of the model, which are less focused on in most studies, are taken as an important step in this paper. [ABSTRACT FROM AUTHOR]

This paper's objective and novelty is to understand the impact of different natural ventilation strategies as well as the effect of a proposed smart controlled ventilation strategy on cooling energy saving and thermal comfort in a residential building. Afterwards, an investigation of the Climatic Potential of Natural Ventilation (CPNV) in hot and dry climates, and particularly the potential of the night ventilation strategy under present and future climates, was made. Two prediction climate change scenarios, RCPs 2.6 and 8.5, were considered for two periods. The results, obtained by a validated simulation, have shown the beneficial effect of night ventilation, since it could reduce cooling load by 9% and would keep the operative temperature below 30°C. Nevertheless, the potential of this natural ventilation strategy would be reduced in the future due to climate change. Indoor overheating has been projected to increase drastically. The summer thermal behaviour will be greatly aggravated in the future. Indeed, the CPNV in the summer nights will be decreased by 25%. However, the climate change will affect the winter natural ventilation potential in the future. The results also highlighted that the annual Total Climatic Potential of Natural Ventilation could be increased by 1% for RCP 2.6 and decreased by 4.9% for RCP 8.5. [ABSTRACT FROM AUTHOR]

Shallow geothermal heat has the characteristics of wide distribution and huge reserves. However, for northern rural buildings, the heating load in winter is much greater than the cooling load in summer, and thermal imbalance of the soil is prone to occur. This paper takes rural residences in southern Hebei as an example and designs a solar-assisted shallow geothermal energy system. Compared with the original shallow ground-source heat pump system, the indoor and outdoor temperature differences of the target building were first analyzed. Second, the changes in the soil temperature field and the differences in the supply and return water temperature were monitored. Finally, the initial investment and operating costs of the assisted system are analyzed. The results show that the average ground temperature field increases significantly in winter. Through the alternate operation of solar energy and ground-source heat pumps, heat extraction from the ground is reduced, and the imbalance cycle of heat extraction in winter and heat removal in summer is shortened. It alleviates the imbalance of the cold and heat loads for soil throughout the year. The inlet and outlet water temperature and heat exchange efficiency of the buried pipes have been significantly improved. The energy efficiency ratio of the system using the assisted system has increased from 3.40 in 2020 to 4.17, an increase of 0.77. The optimal ratio of the solar- assisted shallow geothermal energy system is a 12.80 m2 solar heat-collection area and one buried hole. The winter heating operating cost of the solar-assisted shallow geothermal energy system is 18.86 CNY/m2. It can save 37% of the annual operating costs of heating, cooling, and hot water compared to a single ground-source heat pump system and 40%–45% compared to traditional heating and cooling modes. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology for predicting a warehouse's reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office and storage spaces, and three cooling scenarios are implemented, i.e., exclusive storage area cooling, exclusive office area cooling, and cooling in both spaces, to expand the study's potential applications. Next, the simulation data are utilized for training machine learning (ML)-based pipelines, predicting five subsequent hourly energy consumption values an hour before the setpoint adjustments, providing time to plan participation in demand response programs or prepare for charging electric vehicles. For each scenario, the performance of an Artificial Neural Network (ANN) and a tree-based ML algorithm are compared. Moreover, an expanding window scheme is utilized, gradually incorporating new data and emulating online learning. The results indicate the superior performance of the tree-based algorithm, with an average error of less than 3.5% across all cases and a maximum hourly error of 7%. The achieved accuracy confirms the method's reliability even in dynamic scenarios where the integrated load of storage space and offices needs to be predicted. [ABSTRACT FROM AUTHOR]

The response potential of demand-side resources is becoming increasingly significant in integrated energy system (IES) operations. In addition, to ensure the effective participation of system devices, their actual responsiveness at different timescales should be considered. Based on these considerations, this paper proposes an IES multi-timescale operation optimization strategy that incorporates multiple forms of integrated demand response (IDR) and considers the response characteristics of the equipment. First, the multi-timescale characteristics of IDR are analyzed. Moreover, a multi-timescale operation model of IES that comprises day-ahead, intraday, and real-time stages is further established. In the day-ahead dispatch, a low-carbon economic scheduling model is developed by considering the shifting demand response (DR) and the cost of carbon emissions. In the intraday scheduling, noting that cooling and heat energy transmission possess slow dynamic characteristics, a rolling optimization model for cooling/heating coupled equipment considering load shedding and substituting DR is established. In real-time scheduling, the output of electric/gas coupled equipment is adjusted. Finally, an industrial park-type IES in northern China was selected as an example for a case study. The results show that (1) the IDR multi-timescale response strategy can exploit different types of demand-side flexibility resources. After implementing the shifting DR, the peak-to-valley difference of the electric load curve was reduced by 20%, and the total system cost was reduced by 2.3%. After implementing load shedding, the maximum load differences per unit period of the electric, heat, and cooling load curves decreased by 18.7%, 40.0%, and 68.9%, respectively. (2) By refining the timescale of IES optimization, the proposed model can effectively ensure the energy supply and demand balance of the system under different load scenarios and reduce the system operation cost. After applying the model to simulation in three typical days (transition season, summer, and winter), the penalty costs of lost loads reduce by ¥3650, ¥3807, and ¥3599, respectively, and the total system costs decrease by 17.4%, 16.1%, and 16.2%, respectively. [ABSTRACT FROM AUTHOR]

Determination of thermal insulation performance (i.e. optimum insulation thickness, energy saving and payback period) is a tedious and time-consuming task that requires a thorough knowledge in thermal insulation engineering and economics. The main goal of this paper is to make the determination of insulation performance simple and timesaving by introducing thermal insulation performance curves (TIPCs) from which the insulation performance can easily be found for any climate condition and all economic factors related to energy and insulation. These curves were generated based on a life-cycle cost analysis (LCCA) method. The curves can be easily read based on a single factor, called the f-factor, which comprises the number of degree-day, coefficient of performance, present worth factor, energy cost, and insulation cost. With the gain of heating and cooling degree days (i.e. HDD and CDD), TIPCs can be used for both heating and cooling loads. TIPCs cover commonly used insulation materials for building walls with thermal conductivities range from 0.020 to 0.055 W/m K. TIPCs were validated against published data. [ABSTRACT FROM AUTHOR]

In the quest to minimize energy waste, the energy performance of buildings (EPB) has been a focus because building appliances, such as heating, ventilation, and air conditioning, consume the highest energy. Therefore, effective design and planning for estimating heating load (HL) and cooling load (CL) for energy saving have become paramount. In this vein, efforts have been made to predict the HL and CL using a univariate approach. However, this approach necessitates two models for learning HL and CL, requiring more computational time. Moreover, the one-dimensional (1D) convolutional neural network (CNN) has gained popularity due to its nominal computational complexity, high performance, and low-cost hardware requirement. In this paper, we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D CNN. Considering the building shape characteristics, one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps, a dense layer to interpret the maps, and an output layer with two neurons to predict the two real-valued responses, HL and CL. As the 1D data are not affected by excessive parameters, the pooling layer is not applied in this implementation. Besides, the use of pooling has been questioned by recent studies. The performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error (MSE). Thus, the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE. [ABSTRACT FROM AUTHOR]

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency. [ABSTRACT FROM AUTHOR]

In this paper, the Ant-based Radial Basis Function Network (ARBFN) is proposed to determine the optimal daily dispatch of ice-storage air-conditioning systems. ARBFN is a novel algorithm that is integrated into the Ant Colony Optimization and Radial Basis Function Network. ARBFN is used to construct the function of the cost and operation for each chiller and ice-storage tank and is used to simulate the polynomial function of the cooling load and the cost of power consumption. The best learning rate in the training process is adjusted in ARBFN to improve the accuracy of constructing models for chillers and ice-storage tanks. The electricity savings are thus 4.130% on a summer day and 7.381% on a non-summer day. The results have shown that ARBFN can more accurately calculate the actual power consumption and cooling capability of each chiller and ice-storage tank. Lastly, ACO is used to calculate the daily dispatch of the ice-storage air-conditioning system. The results demonstrated the optimization of energy savings and efficiency for the operation of the ice-storage air-conditioning system. [ABSTRACT FROM AUTHOR]

This paper assesses the dynamic thermal inertia performance of unfired clay bricks with dry-grass fiber additives. The used raw material was retrieved from Ifrane in Morocco. The used clay was illite with a 59.60% silicon proportion. Multiple percentages (0%, 1%, 7%, and 15%) were analyzed in this study. The dynamic thermal inertia simulation was carried out in TRNSYS software to investigate the specimens' time lag, decrement factor, and heating and cooling loads. For this purpose, a reference house with three different wall thicknesses (0.20, 0.25, and 0.30 m) was evaluated. Collected findings reflected great time lag improvements. With the incorporation of 15% additive, 13.11 h was recorded. This reflected a thermal gain of 49.12% with the use of 0.30-m-thick walls. Decrement factor analysis also recorded interesting improvements. Thermal cooling and heating loads also recorded prominent energy savings of 95% and 60%, respectively. Finally, adopting thicker building walls recorded great thermal gains in the range of 60%. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? A hybrid desiccant M-cycle cooling system with an HDH unit is proposed for simultaneous cooling and water production. Three typical operational modes are analysed and compared in terms of cooling and water production. What is the implication of the main finding? The recirculation mode exhibited a superior cooling performance than the other two modes. The water production rates and system COP were similar among the three modes. In this paper, the cooling and freshwater generation performance of a novel hybrid configuration of a solid desiccant-based M-cycle cooling system (SDM) combined with a humidification–dehumidification (HDH) desalination unit is analysed and compared in three operational modes: ventilation, recirculation, and half recirculation. The HDH unit in this system recycles the moist waste air sourced from the M-cycle cooler and rotary desiccant wheel of the SDM system to enhance water production. A mathematical model was established and solved using TRNSYS and EES software. The results of this study indicate that the recirculation mode exhibited superior cooling performance compared to the other two modes, producing up to 7.91 kW of cooling load and maintaining a supply air temperature below 20.85 °C and humidity of 12.72 g/kg under various ambient conditions. All the operational modes showed similar water production rates of around 52.74 kg/h, 52.43 kg/h, and 52.14 kg/h for the recirculation, half-recirculation and ventilation modes, respectively, across a range of operating temperatures. The recirculation mode also exhibited a higher COP compared to the other modes, as the environmental temperature and relative humidity were above 35 °C and 50%. However, it should be noted that the implementation of the recirculation mode resulted in a higher water consumption rate, with a maximum value of 5.52 kg/h when the inlet air reached 45 °C, which partially offset the benefits of this mode. [ABSTRACT FROM AUTHOR]