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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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

In tropical areas of China, energy saving is an important part of architectural design, and the energy-saving potential of residential buildings has attracted extensive attention. This paper studies the daylighting, ventilation, and building energy consumption in tropical areas to find out the best energy-saving performance parameters. The building model is established by grasshopper, and the parameters of daylighting, ventilation performance and cooling load are simulated. The octopus plug-in in grasshopper is used to calculate the target value iteratively, so as to find the relative optimal value of multiobjective. Finally, the optimized design value is compared with the initial value. The results show that the refrigeration energy consumption is greatly reduced from 188.20 kwh/m2 to 163.02 kwh/m2, the Daylight Autonomy (DLA) is reduced from 60.71% to 58.56%, and the ventilation wind speed is increased from 0.62 to 0.63 m/s. It can be seen from the results that although the daylighting objectives was reduced, the cooling energy consumption is greatly reduced, and the optimized daylighting layout is more balanced and reasonable. Therefore, on the basis of reasonable layout, this optimization study effectively reduces the refrigeration energy consumption and achieves the goal of green energy saving. [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]

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]

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]

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]

Aiming at the problems of poor scheduling flexibility and insufficient carbon emission reduction capacity of the current integrated energy system, this paper proposes an optimization scheduling model of integrated energy demand response that combines source-load synergy with reward and punishment stepped carbon trading mechanism. Firstly, the integrated energy system operation architecture including electricity-heat-cooling energy was constructed to harmonize the energy generation side and consumption side and effectively improve the multi-energy complementary capability of the system. Secondly, the reward and punishment stepped carbon trading mechanism is established by combining the initial carbon quota allocation method and the actual carbon emission calculation theory, so that environmental friendliness and economic sustainability are effectively improved. Finally, to minimize the system operating cost, an integrated demand response optimal dispatch model that considers the characteristics of electric, thermal and cooling loads is constructed to determine the optimal dispatch scheme for the integrated energy system. The results of the calculation example indicate that the proposed integrated demand response model has the function of peak reduction and valley correction, energy conservation and carbon reduction, and provides a feasible solution for the integrated energy system optimal scheduling. [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]

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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]

The closed evaporative cooling tower (CECT) is widely used in the field of industrial cooling. At present, most CECTs still mainly adopt the horizontal-tube falling-film cooling method. In this paper, a novel vertical CECT using 3D deformation tubes is developed. To investigate the vertical surface falling-film evaporative cooling effect of this novel cooling equipment, a traditional horizontal CECT was modified to produce a prototype of vertical non-packing CECT. The cooling performance of the novel vertical CECT has been investigated and compared to the previous traditional horizontal CECT by experimental method. The results show that the convective heat transfer coefficient of the water film outside the tube was increased by 5.87~12.95% and the overall cooling performance was increased by 7.31% on average. This indicates that the cooling load can be increased by changing the traditional horizontal-tube falling-film evaporative cooling method to the vertical falling-film evaporative cooling method. Moreover, the heat flux of the novel vertical CECT decreases by about 7% when the wet bulb temperature increases by 1 °C under the test range of wet bulb temperature, which indicates that the ambient wet bulb temperature has an obvious influence on the cooling load. The research results can provide reference for the optimization design of the CECT. [ABSTRACT FROM AUTHOR]

This paper provides a method for predicting the energy efficiency of buildings using artificial intelligence tools. The scopes is twofold: prediction of the levels of the heating load and cooling load of buildings. A feature of this research is the performance of intellectual analysis in conditions of a limited amount of data when solving the stated tasks. An improved method of augmentation and prediction (input-doubling method) is proposed by processing data within each cluster of the studied dataset. The selection of the latter occurs due to the use of the fast and easy-to-implement k-means method. Next, a prediction is made using the input-doubling method within each separate cluster. The simulation of the method was performed on a real-world dataset of 768 observations. The proposed approach was found to have a high prediction accuracy in the absence of overfitting and high generalization properties of the improved method. Comparison with existing methods showed an increase in accuracy by 40-46% (MSE) compared to SVR with rbf kernel, which is the basis for the improved method, and by 5-12% (MSE) compared to the closest existing hierarchical predictor. [ABSTRACT FROM AUTHOR]

During the pre-design stage of buildings, reliable long-term prediction of thermal loads is significant for cooling/heating system configuration and efficient operation. This paper proposes a surrogate modeling method to predict all-year hourly cooling/heating loads in high resolution for retail, hotel, and office buildings. 16 384 surrogate models are simulated in EnergyPlus to generate the load database, which contains 7 crucial building features as inputs and hourly loads as outputs. K-nearest-neighbors (KNN) is chosen as the data-driven algorithm to approximate the surrogates for load prediction. With test samples from the database, performances of five different spatial metrics for KNN are evaluated and optimized. Results show that the Manhattan distance is the optimal metric with the highest efficient hour rates of 93.57% and 97.14% for cooling and heating loads in office buildings. The method is verified by predicting the thermal loads of a given district in Shanghai, China. The mean absolute percentage errors (MAPE) are 5.26% and 6.88% for cooling/heating loads, respectively, and 5.63% for the annual thermal loads. The proposed surrogate modeling method meets the precision requirement of engineering in the building pre-design stage and achieves the fast prediction of all-year hourly thermal loads at the district level. As a data-driven approximation, it does not require as much detailed building information as the commonly used physics-based methods. And by pre-simulation of sufficient prototypical models, the method overcomes the gaps of data missing in current data-driven methods. [ABSTRACT FROM AUTHOR]

An important aspect in improving the energy efficiency of buildings is the effective use of building heating and cooling load prediction models. A lot of studies have been undertaken in recent years to anticipate cooling and heating loads. Choosing the most effective input parameters as well as developing a high-accuracy forecasting model are the most difficult and important aspects of prediction. The goal of this research is to create an intelligent data-driven load forecast model for residential construction heating and cooling load intensities. In this paper, the shuffled shepherd red deer optimization linked self-systematized intelligent fuzzy reasoning-based neural network (SSRD-SsIF-NN) is introduced as a novel intelligent data-driven load prediction method. To test the suggested approaches, a simulated dataset based on the climate of Dhahran, Saudi Arabia will be employed, with building system parameters as input factors and heating and cooling loads as output results for each system. The simulation of this research is executed using MATLAB software. Finally, the theoretical and experimental results demonstrate the efficacy of the presented techniques. In terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, Mean Absolute Error (MAE), coefficient of determination (R2), and other metrics, their prediction performance is compared to that of other conventional methods. It shows that the proposed method has achieved the finest performance of load prediction compared with the conventional methods. [ABSTRACT FROM AUTHOR]

In this paper, an economic investigation is performed to identify the economic feasibility of a solar-powered adsorption cooling system. In the first step, the system is mathematically formulated, and the solar fraction of the system is calculated. After that, the system is economically optimized for a cooling load related to a single-family house using particle swarm optimization. In order to identify the most efficient application of the system, the cost per 1 kW of cooling capacity is calculated over different cooling loads. The results demonstrated that the system under study is more suitable for small-scale applications. In addition, sensitivity to electricity price analysis demonstrates that the main bottleneck of cost-efficient system design is the initial price of the solar collector. Therefore, in the next step, the former sensitivity analysis to electricity price is jointly performed over different initial prices of the solar collector at two different cooling loads. Finally, by considering the cost of CO2 capture and the amount of avoided CO2 by utilizing solar energy, the environmental benefit of the system under investigation is calculated. The results show that the optimum solution is 13% more cost-efficient compared to the base design. In addition, taking the CO2 capture parameters into account, the environmentally friendly case has 21% more annual cost compared to the optimal solution one. However, this extra cost can be justified through CO2 capture cost. [ABSTRACT FROM AUTHOR]

Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 R 2 for heating load prediction and 0.997 R 2 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes. [ABSTRACT FROM AUTHOR]

According to the established model of a single resonance energy selective electron refrigerator with heat leakage in the previous literature, this paper performs multi-objective optimization with finite-time thermodynamic theory and NSGA-II algorithm. Cooling load ( R ¯ ), coefficient of performance (ε), ecological function ( E C O ¯ ), and figure of merit ( χ ¯ ) of the ESER are taken as objective functions. Energy boundary (E ′ / k B) and resonance width (Δ E / k B) are regarded as optimization variables and their optimal intervals are obtained. The optimal solutions of quadru-, tri-, bi-, and single-objective optimizations are obtained by selecting the minimum deviation indices with three approaches of TOPSIS, LINMAP, and Shannon Entropy; the smaller the value of deviation index, the better the result. The results show that values of E ′ / k B and Δ E / k B are closely related to the values of the four optimization objectives; selecting the appropriate values of the system can design the system for optimal performance. The deviation indices are 0.0812 with LINMAP and TOPSIS approaches for four-objective optimization ( E C O ¯ − R ¯ − ε − χ ¯ ), while the deviation indices are 0.1085, 0.8455, 0.1865, and 0.1780 for four single-objective optimizations of maximum E C O ¯ , R ¯ , ε , and χ ¯ , respectively. Compared with single-objective optimization, four-objective optimization can better take different optimization objectives into account by choosing appropriate decision-making approaches. The optimal values of E ′ / k B and Δ E / k B range mainly from 12 to 13, and 1.5 to 2.5, respectively, for the four-objective optimization. [ABSTRACT FROM AUTHOR]

The foundation of energy-efficient architectural design is modeling heating and cooling loads (HLs and CLs), which defines the heating and cooling apparatus constraints necessary to maintain a suitable interior air environment. It is possible that analytical models for energy-efficient buildings might offer an accurate evaluation of the influence that various building designs would have. The implementation of these instruments, however, might be a process that requires a significant amount of manual labor, a significant amount of time, and is reliant on user experiences. In light of this, the authors of this paper present two unique methods for estimating the CL of residential structures in the form of complex mathematical concepts. These methodologies include an evolutionary web algorithm (EWA), biogeography-based optimization (BBO), and a hybridization of an adaptive neuro-fuzzy interface system (ANFIS), namely BBO-ANFIS and EWA-ANFIS. The findings initiated from each of the suggested models are evaluated with the help of various performance metrics. Moreover, it is possible to determine which model is the most effective by comparing their coefficient of determination ( R 2   ) and its root mean square error (RMSE) to each other. In mapping non-linear connections between input and output variables, the observed findings showed that the models used have a great capability. In addition, the results showed that BBO-ANFIS was the superior forecasting model out of the two provided models, with the lowest value of RMSE and the greatest value of R 2   (RMSE = 0.10731 and 0.11282 and R 2   = 0.97776 and 0.97552 for training and testing phases, respectively). The EWA-ANFIS also demonstrated RMSE and R 2   values of 0.18682 and 0.17681 and 0.93096 and 0.93874 for the training and testing phases, respectively. Finally, this study has proven that ANN is a powerful tool and will be useful for predicting the CL in residential buildings. [ABSTRACT FROM AUTHOR]

Aiming at the issues of unreasonable cooperation schemes and inappropriate setting of parameters of the refrigeration system with multi-chiller plants, this paper presents a cooperative optimization method to improve the energy performance of the system composed of water-cooled chillers and air-cooled heat pumps. The cooperative optimization process includes scheme optimization and parameter optimization. To content the dynamic cooling load, the working sequence of air-cooled heat pumps and water-cooled chillers with variable frequency chilled water pumps is first optimized. Based on the optimal scheme, a back-propagation neural network (BPNN) coupled with particle swarm optimization (PSO) is implemented to explore the preferred operating parameters of multiple chiller plants corresponding to the best coefficient of performance (COP). Compared with the performance of the initial operation module, the energy consumption of the water pump and fan decreases by over 50%, and the COP of the refrigeration system is improved by 16% (COP = 3.85) through the scheme operation. After parameter optimization, the total energy consumption is reduced by 21.7%, and COP is increased by 26.5% (COP = 4.20). Therefore, the proposed cooperative optimization method can provide useful operation guidance for the refrigeration system with multi-chiller plants. [ABSTRACT FROM AUTHOR]

User behaviour can significantly affect indoor thermal comfort conditions, as well as energy consumption, especially in existing buildings with high thermal masses where natural cross ventilation is the main strategy to reduce cooling loads. The aims of this paper were: (i) to compare how behavioural changes evaluated by means of rule-based and stochastic models lead to changes in indoor thermal comfort levels, and (ii) to define the patterns of indoor thermal comfort in historic residential buildings in future scenarios. To this end, a historic building located in Molfetta (Southern Italy) was analysed using a dynamic energy simulation engine in five weather scenarios (Typical Meteorological Year, current extreme weather file 2018, predicted weather files for 2020, 2050, and 2080 generated by morphing method), and stochastic and rule-based models for window openings were adopted and compared. The results showed that the stochastic model was more accurate than the rule-based one, resulting in a reduction of discomfort conditions during the summer period between 30% and 50% in all climate scenarios. However, although the differences between predicted discomfort levels using rule-based and stochastic models tended to increase, discomfort levels still appeared to be not acceptable in the 2050 and 2080 scenarios due to the rising temperature driven by climate change. [ABSTRACT FROM AUTHOR]