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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]

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]

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]

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]

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]

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]

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]

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]

The purpose of this research is to design an energy-efficient off-grid vending machine that has a refrigeration system powered by an environmentally friendly refrigerant. The paper presents details from the equipment's design phase in terms of the resistance structure, photovoltaic panels area dimensioning, but also in terms of the necessary cooling load to be provided by the refrigeration system. The refrigerant chosen was R290 following a comparative analysis, which showed that it has an efficiency 6.6% higher than R134a. During the monitoring periods, the outdoors temperature varied between −4–28 °C, the relative humidity between 22–100% and yet only a slight indoor temperature variation of ±1.5 °C compared to the set value was recorded. The battery module managed to store enough energy from the photovoltaic panels to keep the vending machine functional through three consecutive days with adverse environmental conditions, as evidenced by the low variation of indoor temperature [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of the multiple load forecasting of a regional integrated energy system, a short-term multiple load forecasting model based on the quantum weighted GRU and multi-task learning framework is proposed in this paper. Firstly, correlation analysis is carried out using a maximum information coefficient to select the input of the model. Then, a multi-task learning architecture is constructed based on the quantum weighted GRU neural network, and the coupling information among multiple loads is learned through the sharing layer in order to improve the prediction accuracy of multiple loads. Finally, the PSO algorithm is used to optimize the parameters of the quantum weighted GRU. The simulation data of a regional integrated energy system in northern China are used to predict the power and cooling loads on summer weekdays and rest days, and the results show that, compared with the LSTM, GRU and single task learning QWGRU models, the proposed model is more effective in the multiple load forecasting of a regional integrated energy system. [ABSTRACT FROM AUTHOR]

The cooling of server components has been developed over the past few years in order to meet increasing cooling requirements. The growth in performance and power density increases the cooling demand. To gain a better understanding of the evolution and possible future technology developments in the field of data center cooling, a patent analysis method was used with a focus on the server and computer room levels. After the patent extraction from the European patent database for the period 2000–2023, the search results were classified and analyzed. Most of the patents deal with air or liquid cooling. Since 2015, a technological shift from air to liquid cooling can be identified on the level of patent activities. In conclusion, from the patent analysis, it can be derived that liquid cooling will continue to gain in importance in the future and could also be combined with other approaches in the form of hybrid cooling. However, air cooling may still be relevant, even if the main cooling load is handled by liquid-based approaches. At the same time, the optimization potential for air cooling seems to have been largely exploited in comparison to liquid cooling, as can be seen from the falling number of the patent applications. [ABSTRACT FROM AUTHOR]

The prediction of heating and cooling loads using machine learning algorithms has been considered frequently in the research literature. However, many of the studies considered the default values of the hyperparameters. This manuscript addresses both the selection of the best regressor and the tuning of the hyperparameter values using a novel nature-inspired algorithm, namely, the Multi-Objective Plum Tree Algorithm. The two objectives that were optimized were the averages of the heating and cooling predictions. The three algorithms that were compared were the Extra Trees Regressor, the Gradient Boosting Regressor, and the Random Forest Regressor of the sklearn machine learning Python library. We considered five hyperparameters which were configurable for each of the three regressors. The solutions were ranked using the MOORA method. The Multi-Objective Plum Tree Algorithm returned a root mean square error value for heating equal to 0.035719 and a root mean square error for cooling equal to 0.076197. The results are comparable to the ones obtained using standard multi-objective algorithms such as the Multi-Objective Grey Wolf Optimizer, Multi-Objective Particle Swarm Optimization, and NSGA-II. The results are also performant concerning the previous studies, which considered the same experimental dataset. [ABSTRACT FROM AUTHOR]

Ground heat accumulation caused by imbalanced heating and cooling loads in a building can cause the heat-source temperature to increase as the operating age of a geothermal heat pump (GHP) system increases. An alternative system to improve upon this situation is the hybrid GHP system. This study reviews existing research on GHP systems and hybrid GHP systems, variable water flow (VWF) control, and coefficient of performance (COP) prediction. Generally, constant flow control is applied to the circulating pump to provide a flow rate according to the maximum load. The need for VWF control was identified because the hybrid GHP system is used mainly as a heating and cooling heat source system for partial loads rather than the entire building load. Previous studies on predicting the COPs of GHP systems developed prediction models by selecting input values based on mathematical models, collecting data through multiple measurement points, and utilizing data from production environments. The model can be limited by the field environment, and it is necessary to predict the COP using machine learning based on existing field monitoring data. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) have emerged as promising solutions for latent heat thermal energy storage (LHTES) systems, offering considerable potential for storing energy derived from renewable sources across various engineering applications. The present study focused on optimization of solar cooling system by integrating LHTES with different PCM tank configurations. TRNSYS simulation software was selected for the study, and the collected experimental data from laboratory system prototype were used for system validation. The results indicate that the use of PCM led to a noteworthy decrease of 6.2% in auxiliary energy consumption. Furthermore, the time during which the heat carrier temperature flow exceeded 90 °C from the storage tank to the auxiliary fluid heater was extended by 27.8% when PCM was utilized compared to that of its absence. The use of PCM in LHTES is more effective under variable weather conditions. On the day when changes in weather conditions were observed, around 98% of the cooling load was provided by produced sun energy. The results of the research can be used to optimize the solar cooling system, which will help reduce the environmental impact of cooling systems running on non-renewable fuels. [ABSTRACT FROM AUTHOR]

Cooling energy consumption is a major contributor to various sectors in hot climates with a significant number of warm days throughout the year. Buildings account for 40% of total energy consumption, with approximately ∼30–40% of that used for cooling in geographical areas such as Iran. Energy demand for cooling is an important factor in the overall energy efficiency of electric mobility. Electric vehicles (EVs) consume ∼30–50% of energy for the air conditioning (AC) system. Therefore, the efficient management of the cooling demand is essential in implementing energy-saving strategies. Passive radiative cooling is capable of providing subambient cooling without consuming any energy. This article reviews potential applications of passive radiative cooling in reducing cooling energy for buildings. It also provides a rough estimate of the amount of energy saved when applying a radiative cool roof to a model building. It is shown that by using radiative cool materials on roofs, the share of electricity usage for cooling can be reduced to 10%, leading to a reduction in cooling load by 90%. Additionally, the potential use of radiative cool coats of various types for different EV components, such as shell/body, windows, and fabrics, is introduced. Although the prospects of the design and engineering of radiative cooling products appear promising for both buildings and EVs, further investigations are necessary to evaluate scalability, durability, and performance based on factors such as geography and meteorology. [ABSTRACT FROM AUTHOR]

The prescriptive path is the most widely used approach for commercial code compliance in the United States. Though easy to implement, prescriptive approaches do not typically discriminate between minimally compliant, high-performing and poorly performing HVAC system configurations. Hence, to meet aggressive energy and carbon reduction goals, it is clear that energy codes will need to transition from prescriptive to performance-based approaches, a transition that is riddled with several challenges. This paper discusses a new HVAC system-based performance approach (HVAC System Performance) which provides a simpler solution to HVAV system evaluation compared to whole building performance, while keeping tradeoffs limited to specific building systems. The Total System Performance Ratio (TSPR) is a metric for evaluation of overall system efficiency instead of individual component efficiency, a solution which could also eventually facilitate the transition to a 100% performance-based code structure. TSPR is a ratio that compares the annual heating and cooling load of a building to the annual energy consumed by the building's HVAC system. A calculation software tool has been developed for determining a building's TSPR. Already incorporated into the 2018 Washington State Energy Code, this approach is also being evaluated by ASHRAE Standard 90.l Project Committee and has the potential to provide a comprehensive performance-based approach for HVAC system evaluation and analysis. [ABSTRACT FROM AUTHOR]

The vision of decarbonization creates the need to design and construct even more energy-efficient buildings. This current target is even more compelling and challenging. The main issue when designing energy-efficient buildings is to identify present and future building energy requirements. A trending method for solving this problem is dynamic building energy simulation. One of the main inputs during energy simulation is weather data. However, the real problem lies in the fact that standard weather data are good at defining the present situation, and they help in designing buildings that behave efficiently under current climate conditions. To achieve the goal of constructing climate proof buildings, the Weather Research and Forecast meteorological model (WRF) was used to predict future climate scenarios. At first, data from previous years (2006–2010) were used to represent the current climate. The model was used to generate future climate data. Thus, results were produced for 5 year periods 2046–2050 and 2096–2100. These data were used for the energy simulation of an office building in Thessaloniki, Greece. The simulation results showed a reduction in heating loads by approximately 20% in the long term and a simultaneous impressive increase in cooling loads by 60%, highlighting the inadequacy of the existing building shell, as well as the heating, ventilation, and air-conditioning (HVAC) system design. [ABSTRACT FROM AUTHOR]

Thermal energy storage using phase change materials (PCMs) is a promising technology for improving the thermal performance of buildings and reducing their energy consumption. However, the effectiveness of passive PCMs in buildings depends on their optimal design regarding the building typology and typical climate conditions. Within this context, the present contribution introduces a novel multiobjective computational method to optimize the thermophysical properties of cementitious building panels enhanced with a microencapsulated PCM (MPCM). To achieve this, a parametric model for PCM-based cementitious composites is developed in EnergyPlus, considering as design variables the melting temperature of PCMs and the thickness and thermal conductivity of the panel. A multiobjective genetic algorithm is dynamically coupled with the building energy model to find the best trade-off between annual heating and cooling loads. The optimization results obtained for a case study building in Sofia (Bulgaria-EU) reveal that the annual heating and cooling loads have contradictory performances regarding the thermophysical properties studied. A thick MPCM-enhanced panel with a melting temperature of 22 ° C is needed to reduce the heating loads, while a thin panel with a melting temperature of 27 ° C is required to mitigate the cooling loads. Using these designs, the annual heating and cooling loads decrease by 23% and 3%, respectively. Moreover, up to 12.4% cooling load reduction is reached if the thermal conductivity of the panels is increased. Therefore, it is also concluded that the thermal conductivity of the cement-based panels can significantly influence the effectiveness of MPCMs in buildings. [ABSTRACT FROM AUTHOR]

Energy consumption in the building sector is a major concern, particularly in this time of worldwide population and energy demand increases. To reduce energy consumption due to HVAC systems in the building sector, different models based on measured data have been developed to estimate the cooling load. The purpose of this work is to develop a linear regression model for cooling load of a research room based on the radiant time series (RTS) components of the cooling load that consider the building material and the environment. Using the forward step method, linear regression models were developed for both all-seasons and seasonal data from three years of cooling load data obtained from the RTS method for a research room at Mangosuthu University of Technology (MUT), South Africa. The male and female occupants, window cooling load, and roof cooling load were found to be the most influential predictors for the cooling load model. The obtained relative errors between the best all-seasons model and seasonal models built with the same predictors for the respective data subsets are almost zero and are given as 0.0073% (autumn), 0.0016% (spring), 0.0168% (summer), and 0.0162% (winter). This leads to the conclusion that the seasonal models can be represented by the all-seasons model. However, further study can be performed to improve the model by incorporating the occupancy behaviours and other components or parameters intervening in the calculation of cooling load using the radiant time series method. [ABSTRACT FROM AUTHOR]

In this work, we present a three-stage multiobjective mixed-integer linear programming (MILP) for the optimal expansion planning and operation of isolated multienergy microgrids in remote areas. By selecting the optimal distributed generators (DGs) and energy storage systems (ESSs) mix selection, siting, sizing, and scheduling in the remote microgrid, the proposed model is targeted to minimize the annualized total cost of microgrids while enhancing the performance of the system, i.e., minimizing the voltage deviations and line power loss. To represent the electricity and heat flow between generation resources and various electrical, heating, and cooling loads in the isolated microgrid, linearized power flow, and heat flow constraints are employed in the proposed optimization model. The available capacity of DGs and ESSs are modeled as discrete constants instead of continuous variables for practical purpose. Numerical simulation results on a remote microgrid consisting of DGs, ESSs, and various loads validate the proposed method. [ABSTRACT FROM AUTHOR]

Increasingly, the exploitation of buildings involves the need for comprehensive management of all systems operating within it, their continuous monitoring, both for the safety of users and to mainly optimize the operating parameters of systems that are largely dependent on each other, interact with each other. This is mainly associated with the need for the most energy-efficient management of energy consumption, which has the largest share of operating costs. The main cost for the facilities is the cooling aspect, which is significant, and for this reason, it is necessary to analyze the possibility of its minimization. One of the possible systems to consider in order to reduce the consumption of cooling energy in the facility is the application of free-cooling technology, which is based on the use of the natural cooling capacity of the outside air for cooling rooms with high heat gains. The application of the free cooling system for facilities with a relatively stable cooling load, with significant heat gains from equipment is justified due to the possibility of maintaining low room temperatures, below the comfort value for people, for a long period of time. The problem arises when a large proportion of the heat gains are from people. In such a situation, cooling of rooms is possible but requires a large amount of ventilation air, which is already a serious limitation on the one hand because of the existing air conditioning equipment, lack of possibility to increase the ventilation air stream; on the other hand, the low heat capacity of the air itself, compared to, e.g., water systems used for cooling of the facility or multisplit systems. These limitations put a question mark over the possibility of adapting the technology of free cooling for the existing facilities, where the majority of heat gains come from people. It is not possible in such objects to lower the room temperature below the limit values for the sake of thermal comfort, which in itself limits the possibilities of using air as a natural cooling agent in such objects. Based on the measurement data obtained from the object, it was decided to check the possibilities of applying free-cooling technology in a large shopping facility and obtaining an answer as to what extent it is possible to reduce the power of cooling units with the application of free-cooling exchangers. [ABSTRACT FROM AUTHOR]

Climate change is an important issue that affects energy consumption, causes health problems, such as heat stroke, and requires urgent countermeasures. Serious health problems, including cardiac arrest, often occur in winter in traditional residences in Japan. Cooling-heating energy is required to maintain a healthy thermal environment. Although energy efficiency standards for buildings have been introduced worldwide to reduce energy consumption and various passive energy-saving methods are being investigated, traditional residences still face difficulties in conducting renovations because of various restrictions, such as the conservation of historical or aesthetic values. In this study, these issues and their appropriate countermeasures were investigated for a traditional townhouse in Kyoto, Japan, "Kyo-machiya" (including its new form "Heisei-no-Kyo-machiya"). The potential of reducing heating and cooling loads was examined by conducting numerical analysis considering residents' lifestyles. Field surveys of the indoor environment were conducted in both summer and winter. It was revealed that by optimizing the times and positions of opening and closing the windows and indoor partitions, the indoor air flow could be adjusted from both thermal comfort (cooling in summer) and discomfort (cold drafts in winter) perspectives, leading to improving the indoor environment without using energy. [ABSTRACT FROM AUTHOR]

This article presents approximating relations defining energy-optimal structures of the HVAC (Heating, Ventilation, Air Conditioning) system for cleanrooms as a function of key constant parameters and energy-optimal control algorithms for various options of heat recovery and external climates. The annual unit primary energy demand of the HVAC system for thermodynamic air treatment was adopted as the objective function. Research was performed for wide representative variability ranges of key constant parameters: cleanliness class—Cs (ISO5÷ISO8), unit cooling loads— q ˙ j (100 ÷ 500) W/m2 and percentage of outdoor air—αo (5 ÷ 100)%. HVAC systems are described with vectors x ¯ with coordinates defined by constant parameters and decision variables, and the results are presented in the form of approximating functions illustrating zones of energy-optimal structures of the HVAC system x ¯ * = f (Cs, q ˙ j , αo). In the optimization procedure, the type of heat recovery as an element of optimal structures of the HVAC system and algorithms of energy-optimal control were defined based on an objective function and simulation models. It was proven that using heat recovery is profitable only for HVAC systems without recirculation and with internal recirculation (savings of 5 ÷ 66%, depending on the type of heat recovery and the climate), while it is not profitable (or generates losses) for HVAC systems with external recirculation or external and internal recirculation at the same time. [ABSTRACT FROM AUTHOR]

Increasing peak power demand implies the increasing significance of energy storage. Technologies that efficiently store heat and cold are also important for increasing the share of renewables and improving the efficiency of heating, ventilation, and air conditioning (HVAC) systems. The present experimental study investigated the dynamic behavior of a room with suspended thermally activated ceiling panels filled with a material containing 60% paraffin. The purpose of the study was to determine the specific cooling power and the total energy supplied to the phase change material (PCM) during regeneration. Convective heat flux density, radiant heat flux density, and the heat transfer coefficient (convective, radiant) at the ceiling surface were calculated. Analysis shows that shifting system activation to use lower temperatures during the night maintains thermal comfort. [ABSTRACT FROM AUTHOR]

Sugar alcohols have interesting thermodynamic properties that make them good options as heat storage materials (HSMs) to be used in solar cookers. Among sugar alcohols, xylitol is affected by severe supercooling that can significantly alter its usefulness in thermal energy storage (TES) systems. To overcome the supercooling issue, in this work the thermal behavior of a xylitol-based TES installed in a portable solar box cooker was investigated experimentally. The solar cooker has a 4.08 concentration ratio and the TES is a double-pot system filled with 2.5 kg of commercial-grade xylitol. The TES includes a manual mixing device that can be used to trigger the nucleation of xylitol. The effectiveness of the TES system with and without triggering was assessed through several outdoor tests, divided into heating and cooling phases, using silicone oil as absorbing media. It was found that the average load cooling time, in the temperature range of the test fluid from 110 to 80 °C, increased by about 346% when the solar cooker was equipped with the xylitol-triggered TES. The mixing device can therefore be considered an effective solution for regarding xylitol as an actual and performing phase change material. [ABSTRACT FROM AUTHOR]

Urban heat island (UHI) effects significantly impact building energy. Traditional UHI investigation methods are often incapable of providing the high spatial density of observations required to distinguish small-scale temperature differences in the UHI. Crowdsourcing offers a solution. Building cooling/heating load in 2018 has been estimated in London, UK, using crowdsourced data from over 1300 Netatmo personal weather stations. The local climate zone (LCZ) scheme was used to classify the different urban environments of London (UK). Inter-LCZ temperature differences are found to be generally consistent with LCZ temperature definitions. Analysis of cooling degree hours in July shows LCZ 2 (the densest urban LCZ in London) had the highest cooling demand, with a total of 1550 cooling degree hours. The suburban related LCZs 5 and 6 and rural LCZs B and D all had about 80% of the demand of LCZ 2. In December, the rural LCZs A, B and D had the greatest heating demand, with all recording around 5750 heating degree hours. Urban LCZs 2, 5 and 6 had 91%, 86% and 95% of the heating demand of LCZ D, respectively. This study has highlighted both advantages and issues with using crowdsourced data for urban climate and building energy research. [ABSTRACT FROM AUTHOR]