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Based on the non-equilibrium thermodynamics and energy and exergy analyses, a thermodynamic model of two-stage thermoelectric (TE) cooler (TTEC) driven by two-stage TE generator (TTEG) (TTEG-TTEC) combined TE device is established with involving Thomson effect by fitting method of variable physical parameters of TE materials. Taking total number of TE elements as constraint, influences of number distributions of TE elements on three device performance indictors, that is, cooling load, maximum COP and maximum exergetic efficiency, are analyzed. Three number distributions of TE elements are optimized with three maximum performance indictors as the objectives, respectively. Influences of hot-junction temperature of TTEG and cold-junction temperature of TTEC on optimization results are analyzed, and difference between optimization results corresponding to three performance indicators are studied. Optimal performance intervals and optimal variable intervals are provided. Influences of Thomson effect on three general performance indicators, three optimal performance indicators and optimal variables are comparatively discussed. Thomson effect reduces three general performance indicators and three optimal performance indicators of device. When hot- and cold-junction temperatures of TTEG and TTEC are 450, 305, 325 and 295 K, respectively, Thomson effect reduced maximum cooling load, maximum COP and maximum exergetic efficiency from 9.528 W, 9.043×10 −2 and 2.552% to 6.651 W, 6.286×10 −2 and 1.752%, respectively.

Nowadays, air refrigerator possesses some potential for applications. Based on finite-time thermodynamics, an irreversible simple Brayton refrigeration cycle is taken as research object herein. Firstly, efficient cooling load ( Ω )(product of cooling load ( R ) and coefficient of performance ( ε )) is selected as optimization objective. The relationship among Ω and pressure ratio ( π ) and heat conductance distribution ( u ) is obtained by numerical calculations. Secondly, taking π and u as optimal variables, and taking ε , R , ecological function, Ω and their combinations as optimization objectives, and multi-objective optimizations are performed via NASG-II. Totally fifteen optimization problems, including one quadruple-objective, four triple-objectives, six double-objectives and four single-objectives optimization problems, are solved. Results show that optimization results of Ω ensure coordination between R and ε . Three decision-making strategies of TOPSIS, LINMAP and Shannon entropy are utilized to ascertain final solutions, and TOPSIS and LINMAP decision-making strategies have the best results when quadruple-objective optimizations are performed. The solution selected by LINMAP decision-making strategy is the best when double-objective optimization of ε and ecological function is performed. Multi-objective optimization can solve the problem of conflicting optimization results among multiple optimization objectives. It can find the best scheme for improving the performance of refrigerator, which can achieve the best trade-off of performance objectives.

Heating load and cooling load forecasting are crucial for estimating energy consumption and improvement of energy performance during the design phase of buildings. Since the capacity of cooling ventilation and air-conditioning system of the building contributes to the operation cost, it is ideal to develop accurate models for heating and cooling load forecasting of buildings. This paper proposes a machine-learning technique for prediction of heating load and cooling load of residential buildings. The proposed model is deep neural network (DNN), which presents a category of learning algorithms that adopt nonlinear extraction of information in several steps within a hierarchical framework, primarily applied for learning and pattern classification. The output of DNN has been compared with other proposed methods such as gradient boosted machine (GBM), Gaussian process regression (GPR) and minimax probability machine regression (MPMR). To develop DNN model, the energy data set has been divided into training (70%) and testing (30%) sets. The performance of proposed model was benchmarked by statistical performance metrics such as variance accounted for (VAF), relative average absolute error (RAAE), root means absolute error (RMAE), coefficient of determination (R 2 ), standard deviation ratio (RSR), mean absolute percentage error (MAPE), Nash–Sutcliffe coefficient (NS), root means squared error (RMSE), weighted mean absolute percent error (WMAPE) and mean absolute percentage Error (MAPE). DNN and GPR have produced best-predicted VAF for cooling load and heating load of 99.76% and 99.84% respectively.

PurposeBuildings consume a large amount of energy for space cooling during the summer season, creating an overall sustainability concern. The upfront cost associated with sustainability repels the decision-makers to often end up adopting solutions that have huge operations and maintenance costs. Therefore, the purpose of this study is to assess the lifecycle cost (LCC) implications of optimum configurations of active and passive strategies for reducing the cooling load in buildings.MethodsSeveral green building active and passive strategies and technologies were assimilated and their thermal performance in a hot semi-arid climate of Lahore in Pakistan using DesignBuilder V6.1 was simulated to obtain the most optimum cooling load configuration. Furthermore, LCC is estimated, and overall efficiency is evaluated to identify the most effective space cooling configuration.Results and discussionThe results suggest that a configuration of EPS for external wall insulation, vertical louvers for external shading, 6 mm blue HRG (low-E soft coated) + 12 mm air space + 6 mm clear glass for windows, polystyrene as roof insulation, cross ventilation through windows, and LED lighting system has the best performance. This is the first-of-its-kind study in the hot semi-arid climate of South Asia with the city of Lahore in Pakistan as the test case and can be generalized for places with similar conditions. The findings will help the decision-makers in selecting the most load-efficient and cost-effective green building technologies to help improve overall sustainability.ConclusionThe implementation of the proposed strategies not only aids in providing user-friendly and effective decision-making but also promotes the adoption of sustainability in buildings by leveraging the existing green building technologies to enhance the environmental and economic aspects. This is a promising approach to facilitate the spread of green building construction in developing countries. It is recommended to utilize the strategies grouped in Scenario 8 to achieve a reduced cooling load and LCC of a residential building throughout its lifecycle.

Energy efficiency is currently a hot topic in engineering due to the monetary and environmental benefits it brings. One aspect of energy efficiency in particular, the prediction of thermal loads (specifically heating and cooling), plays a significant role in reducing the costs associated with energy use and in minimising the risks associated with climate change. Recently, data-driven approaches, such as artificial intelligence (AI) and machine learning (ML) techniques, have provided cost-effective and high-quality solutions for solving energy efficiency problems. This research investigates various ML methods for predicting energy efficiency in buildings, with a particular emphasis on heating and cooling loads. The review includes many ML techniques, including ensemble learning, support vector machines (SVM), artificial neural networks (ANN), statistical models, and probabilistic models. Existing studies are analysed and compared in terms of new criteria, including the datasets used, the associated platforms, and, more importantly, the interpretability of the models generated. The results show that, despite the problem under investigation being studied using a range of ML techniques, few have focused on developing interpretable classifiers that can be exploited by stakeholders to support the design of energy-efficient residential buildings for climate impact minimisation. Further research in this area is required.

In the present work, CuS–ZnO/water hybrid nanofluids (in concentrations of 0.0025 mass% and 0.005 mass%) are synthesized using a two-step method with nanoparticles composition of 95% CuS and 5% ZnO. The optically tuned nanofluid filter on the agricultural greenhouse roof can reduce the cooling load by transmitting the visible spectrum and absorbing the near-infrared radiation in the solar spectrum. The size distribution of nanoparticles, stability and optical transmission of both concentrations in the visible and near-infrared regions are examined. Two hollow containers (i.e., ducts) with thicknesses of 4 mm and 8 mm are prepared. Each of these ducts is attached to a greenhouse unit and placed in front of a solar simulator. The experimental results reveal that applying CuS–ZnO nanofluid reduces the inside temperature of the greenhouse unit under all irradiance and ambient temperature ranges. The cooling system gains an average of 27.4% less heat from the greenhouse unit when the CuS–ZnO nanofluid flows through an 8 mm duct compared to no-fluid case (empty duct). The photothermal conversion efficiency of nanofluid is found to be higher than the one for water. The crop growth factor of 82.2% is obtained for 8 mm duct case, and the photosynthetic photon flux density inside the greenhouse unit is reduced without affecting the growth of many plants. Furthermore, the payback period of the nanofluid system (with 8 mm duct) is calculated as 0.42 years, and the application of optically tuned nanofluid can help reduce the cooling system's size and energy requirement for cooling.

Due to their benefits in interior thermal comfort, energy saving, and noise reduction, radiant cooling systems have received wide attention. Radiant cooling systems can be viewed as a part of buildings’ maintenance structure and a component of cooling systems, depending on their construction. This article reviews studies on heat exchange in rooms utilizing radiant cooling systems, including research on conduction in radiant system structures, system cooling loads, cooling capacity, heat transfer coefficients of cooling surfaces, buildings’ thermal performance, and radiant system control strategy, with the goal of maximizing the benefits of energy conservation. Few studies have examined how radiant cooling systems interact with the indoor environment; instead, earlier research has focused on the thermal performance of radiant cooling systems themselves. Although several investigations have noted variations between the operating dynamics of radiant systems and conventional air conditioning systems, the cause has not yet been identified and quantified. According to heat transfer theory, the authors suggest that additional research on the performance of radiant systems should consider the thermal properties of inactive surfaces and that buildings’ thermal inertia should be used to coordinate radiant system operation.

•A supply-side cold load regulation methodology (SSRM) was proposed.•Based on SSRM, its evaluation indexes were proposed.•A theoretical, simulation and experimental analytical model was developed.•The effects of human-controllable and uncontrollable factors were discussed. The air-conditioning system of an office building may have much higher operational energy consumption than its designed value due to the actual use behavior which significantly deviates from the predicted one. To precisely analyze the actual air-conditioning energy consumption, in this paper, based on the impact of occupant air-conditioning behavior on the energy consumption of air-conditioning systems, a Supply-Side Cooling Load Regulation Method (SSRM) is proposed, and its air-conditioning energy consumption analysis model is established. The energy consumption characteristics of the air-conditioning system under SSRM were analyzed, with the assumption that the example office building is located in a hot summer and cold winter areas. The results indicate that the indoor air conditioning set temperature and the actual fresh air volume are important factors affecting the variation of the air conditioning system cooling load under the Demand-Side Cooling Load Regulation Method (DSRM) method, whereas under SSRM, the air conditioning system cooling load is not affected by the variation of the indoor set temperature and the fresh air volume; meanwhile, regulating human air-conditioning use behaviors is the key point of reducing air-conditioning cooling load. The increasing fresh air volume from 30 m3/(p·h) to 90 m3/(p·h) can result in about a 5 °C increase in the average daily indoor air temperature in the rooms used by SSRM, and it also causes an increase in the total air-conditioning load in the rooms under DSRM by more than 45 kWh/day. Further, compared to the DSRM, the SSRM can well regulate occupant air-conditioning use behaviors, which reduces energy consumption by more than 35 % in the summer. [Display omitted]

Energy consumption in buildings is considerably high in areas of hot and humid climates due to its association with high cooling loads. Electricity grids are highly affected by the consumption of cooling systems like air-conditioning and large refrigeration facilities, which significantly impact the economic and environmental sectors. As building design and operating parameters influence the cooling demand in the building, it is believed the root cause of the problem may be detected at an early building design stage. Thus, this review identifies the building design parameters that impact the cooling loads in build- ings that are geographically restricted to countries with hot and humid climates. The building’s design characteristics are classified into four main categories: glass characteristics, wall characteristics, building orientation and dimensions (BO & D), and building cooling system. The review was conducted over high-rise and low-rise buildings. Annual energy requirements (in some cases overlapping with electricity consumption), annual cooling loads, and peak cooling loads are the three forms in which energy demand reductions in buildings are represented. It is found that maximum annual cooling load savings are obtained through cooling systems, followed by wall characteristics, then glass characteristics, with the least for BO & D, with maximum reductions of up to 61%, 59%, 55%, and 21%, respectively. As for the peak cooling load reductions, wall characteristics, cooling systems, and glass characteristics had almost the same average values of 18.7%, 15.2%, and 17.2%, respectively, while BO & D are not reported due to the incomparable number of case studies. The parameters that have the most influence on reductions in peak cooling loads are wall and glass characteristics. In general, savings that are associated with wall characteristics are more significant for low-rise buildings than for high-rise buildings, while the latter is more influenced by glass characteristics. This is a reasonable conclusion since high-rise buildings, in general, acquire higher window-to-wall ratios than the former. In general, most studies considered glass characteristics, while fewer studies considered BO & D. This review has shown various aspects that are vital in studying building cooling load demand and its related energy performance.

Thermal comfort in public buildings is crucial for occupant well-being and energy efficiency. This study employs TRNSYS software to simulate the effects of thermal mass, window performance, and window–wall ratio (WWR) on summer thermal comfort. The results indicate that without energy-saving measures, increased thermal mass raises daily average maximum and minimum temperatures by 0.33–0.96 °C and 0.14–0.94 °C, respectively. Enhanced WWRs lead to higher daily average maximum and minimum temperatures for double-glazed windows (0.18–0.61 °C and 0.07–0.62 °C, respectively), while single-glazed windows show increased maximum temperatures (0.18–1.86 °C) but decreased minimum temperatures (−0.01 to −0.72 °C). Thermal mass has a modest effect on indoor overheating during high outdoor temperatures. Double-glazed windows and lower WWRs effectively reduce indoor overheating, decreasing the attenuation coefficient by 2.13–28.94%. Conversely, single-glazed windows and higher WWRs enhance heat dissipation, increasing daily average temperature fluctuations by 2.33–44.18%. Notably, single-glazed windows with WWRs ≥ 50% improve thermal comfort by reducing extreme superheat temperature occurrence in heavy-thermal-mass buildings by 0.81 to 14.63%. Despite lower cooling loads with heavy thermal mass, double-glazed windows, and low WWRs, the study suggests that single-glazed windows and high WWRs can enhance summer thermal comfort. Therefore, reasonable shading measures and lighter thermal mass are recommended for such buildings.