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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]

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

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

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

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

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]

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

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]

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]

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]

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 traditional air-conditioning system, low-temperature chilled water is used to cool air. Generally, the temperature difference between air to be operated and the chilled water is high, and majority of air can be operated using water at higher temperatures. Therefore, this paper proposes the concept of grade of load and the method of dividing cooling/heating load into different grades. A traditional air cooling/heating load and energy consumption of fresh air handling unit (FAHU) in Beijing, were compared with cooling/heating loads with different grades and energy consumption of FAHU with different grade energies. The results indicate that cooling and heating loads, handled by the lowest and highest water temperatures of 9.5°C and 37.5°C, account for 27% and 25% of cooling and heating loads in design conditions, respectively. The cumulative cooling/heating load handled by water temperature with highest grade, only accounts for 47%/35% of the total cumulative cooling/heating load. As compared to traditional air handling process, the energy-saving rate of FAHU using different grade energies is 16.4% in summer and 25.6% in winter. This study shows that handling air with different grade energies has significant energy-saving potential for air-conditioning system. [ABSTRACT FROM AUTHOR]

The skylight on the roof of an atrium can be popular for commercial malls to illuminate the core area of the building. However, the solar radiation and its heat can get into the building together with the daylight, causing excessive cooling load. This paper studies the daylighting and energy performances of skylight coverage area for the air-conditioned atriums in the hot and humid regions. The energy performance with different atrium heights, glass types and the coverage ratios of the skylight are studied. The daylight performance was simulated by the ray-tracing Radiance and was transferred into EnergyPlus for energy evaluations. The finding suggested that, for hot and humid climates, the skylight coverage ratio should be controlled carefully to prevent the excessive solar heat gain. When the on/off lighting control is applied, the total energy consumption of the single-floor cases (or of the top floor for the multi-floor cases) leveled off when the coverage ratio of the skylight reached 9%. Thus, the skylight is favorable to the energy saving of the low-rise or single-floor commercial buildings only under the current assumptions, as the ground of the atrium cannot be well illuminated while the excessive solar radiation gets into the building. The skylight should be shaded in cooling seasons to prevent the excessive solar heat gains. [ABSTRACT FROM AUTHOR]

Due to the higher energy efficiency as well as the higher flexibility and reliability of energy supply, multi-carrier energy systems (MCESs) have attracted extensive attention. However, the power imbalance caused by uncertainty, the complicated couplings among multiple carriers, and the communication obstacles among multiple MCESs bring challenges to the synergistic scheduling of multiple MCESs. To this end, this paper studies the synergistic scheduling of multi-MCES coupled with electricity, natural gas, heating, and cooling carriers, considering integrated demand response (IDR) program and load volatility. First, an IDR program with electricity, heating, and cooling loads is proposed, where the consumers can change their energy consumption patterns in response to price signals to reduce operating costs. Second, to smooth the electricity load curve and avoid load fluctuations, load volatility is taken into account when implementing the IDR program. In addition, the non-convex constraints of natural gas network are tackled via second-order cone relaxation, and the scheduling of multi-MCES is reformulated as mixed integer second-order cone programming (MISOCP). Finally, the synergistic scheduling model of multi-MCES considering IDR program and load volatility is solved by consensus-based alternating direction method of multipliers with limited boundary information exchange with neighboring MCESs. Numerical results illustrate the effectiveness of the proposed distributed IDR program in a multi-MCES. [ABSTRACT FROM AUTHOR]

K-12 schools are the largest energy consumers in the public sector, with their HVAC energy consumption representing the largest portion of their total energy use. While transitioning these schools to grid-interactive HVAC system operation through advanced controls offers significant financial and environmental benefits, and model predictive control (MPC) has been identified as a promising solution to achieve that, very few MPCs are affordable and have been deployed in K-12 schools. This situation raises concerns about the unclear real-world benefits of MPC technology among facility managers and industries. To address this gap, this paper presents a low-cost MPC solution that requires minimal control infrastructure costs and a unique field demonstration at a K-12 school, conducted for both cooling and heating seasons. This work adopted a previously developed MPC and extended it for use in the school application. The MPC aims to coordinate multiple packaged units to eliminate unnecessary peaks and shift cooling or heating loads in response to grid signals based on load conditions, while maintaining thermostat temperatures within school-defined bounds. Throughout the field tests, the MPC achieved a 24% reduction in peak demand during the cooling season and shifted cooling or heating loads by up to 16% in response to the school's utility tariff, considering load conditions, while also allowing end-users to override thermostat setpoints. The paper also discusses the limitations of this study and future research directions for better performance of the MPC at K-12 schools. • First MPC demonstration in K-12 school buildings for peak demand reduction and load shifting. • Experimentally demonstrated a scalable MPC for multiple RTUs without hardware retrofit. • Hierarchical MPC is improved in consideration of practical challenges such as user's override and poor data resolutions. • Achieved 24% peak demand reduction and showed 16% of load shifting potential. • Share lessons learned during the demonstration of practical challenges. [ABSTRACT FROM AUTHOR]

With the rapid development of global informatization, data center computing tasks are increasing, and the resulting energy consumption problems are particularly prominent in the context of carbon emission reduction, and affect the safe and green operation of data centers. Aiming at the problem of high and large fluctuation energy load of data center servers, this paper presents a method of optimizing data center servers load considering spatial-temporal load distribution of computing resources. Co-optimized allocation of computing resources and load is studied with examples from the property that computing resources is adjustable in time and space dimensions. The results show that the proposed method is able to reduce the peak electric and cooling loads of the data center room by 10.7 %, reduce the total annual electric consumption by 2.66 % and the total annual cooling consumption by 2.46 %, and reduce the economic and environmental costs of energy supply for the data center in the case study, which comprehensively shows that the load optimization of data center servers has been achieved through the optimal allocation of computing resources. [ABSTRACT FROM AUTHOR]

This paper investigates the simultaneous effect of orientation and thermal insulation of the building on each other and on the heating and cooling loads of buildings. For this aim, the heating and cooling loads of a sample building are calculated with different orientations and amounts of thermal insulation in multiple cities in Iran, which have different climatic conditions. The optimum building orientation in all the surveyed cities is when the main façade faces south, which can reduce the energy consumption of the building by 11–39%. This reduction was greater in cities with higher cooling-degree-days (CDD); which means the effect of orientation on the cooling load of a building is more significant than its effect on the heating load. Increasing the thermal insulation of the exterior walls also reduced the energy consumption of the building. 5–10% reduction in total energy consumption of the building was observed by decreasing the u-value of external walls from 0.8 to 0.4 W/m2K. This reduction was higher in cities with higher heating-degree-days (HDD) which means that the effect of insulation on heating energy consumption is more significant than its effect on cooling energy consumption. In addition, the results indicate that the building orientation and thermal insulation improve each other's efficiency. Highlights The effect of building orientation on energy consumption was investigated. Building envelope insulation effect on heating and cooling demands was investigated. The simultaneous effect of building orientation and insulation was analyzed. The results were discussed concerning different climates and climatic conditions. [ABSTRACT FROM AUTHOR]

• A comparison method between energy-only and energy and reserve model. • Higher revenue for flexible technologies was achieved for joint model. • The down reserve proved to be more required in high RES systems. • The study showed significant difference in operation between two models. The requirements for ancillary services in energy systems are growing as the amount of renewable energy sources such as solar and wind increases. On the other hand, the system can benefit from extra sources of flexibility thanks to the increased digitalization that enables successful integration of demand response and energy storage. The majority of recent energy system analysis research did not consider the regulation markets. As a result, these investigations preclude the ability to fully evaluate the advantages of energy storage and flexible cooling loads. With the method outlined in this paper, it is possible to compare the two strategies and assess the advantages of energy storage and flexible cooling loads for each strategy. In addition to the energy storage systems, the case study in this paper focuses on flexible cooling loads as a significant consumer in the system. According to the findings, the system's operating costs increased by 19.4% when reserve limitations were placed. Furthermore, the findings demonstrated that when flexible assets were permitted to participate in the reserve market, they generated significantly higher incomes. According to the findings, mechanisms that will allow energy storage and demand response to provide reserve capacities as well as the growth of reserve markets are both necessary. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The heat rejected by outdoor units of split A/C conditioners can impact the ambient outdoor environment of mixed-mode buildings. Nevertheless, how this environmental impact may affect the space-conditioning energy use and indoor air pollution is poorly understood. By coupling EnergyPlus and Fluent, this study examines the effects of outdoor units' heat rejection on the building surroundings, building cooling load, and indoor PM2.5 exposure of a six-storey mixed-mode building. The building had an open-plan room on each floor, with the outdoor unit positioned below the window. The coupled model was run for a selected day when the building was cooled by air conditioning and natural ventilation. Five mixed-mode cooling strategies were simulated, reflecting different window-opening schedules, airflow rates of outdoor units, and cooling set-points. The results indicate that compared with the always-air-conditioned mode, the mixed-mode operation could significantly mitigate the negative impact of heat rejection on space-cooling energy consumption. Increasing the airflow rate of outdoor units led to a lower increase in demand for space cooling and lower indoor PM2.5 exposure. If one of the six rooms needs to be cooled to a lower temperature than the others; choosing the bottom-floor room helped achieve more energy savings and better indoor air quality. [ABSTRACT FROM AUTHOR]

In this paper, a thermoelectric air-cooling system was used to cool down the airflow window glazing surfaces during summer in hot climates by which cooling load of the indoors and occupant's thermal discomfort near the window reduce. The performance of the proposed system was modeled analytically, in which the models used were validated by the literature experiment results. To determine the thermoelectric system specifications, the three features of the system including the time working interval, the number of modules, and the degree of air temperature attenuation were investigated. The results show that using the thermoelectric air cooling system for the limited time interval within hours of peak cooling load can significantly reduce the energy consumption, while using the system for a longer time interval not only cannot decrease the energy consumption but also may increase. Besides, the results reveal that the thermoelectric system with 15 modules is required to be energy efficient. On the other hand, an increase in the number of modules more than 20 has no considerable effect on energy saving. Furthermore, the percentage of energy saving is 6.5% for 5 °C air cooling and reached a maximum of 7.1% for 7 °C air cooling, while for 10 °C, this value is zero. The mean reduction of the maximum interior glazing surface temperature is 5.9 and 7.4 °C for air cooling degrees of 5 and 10 °C. [ABSTRACT FROM AUTHOR]

A building-integrated photovoltaic-thermal (BIPVT) system integrates building envelope and photovoltaic-thermal collectors to produce electricity and heat. In this paper, the electrical and thermal performance of roof-based BIPVT systems developed in the recent two decades and their effects on heating and cooling load of the building are reviewed. According to the use of thermal energy from the photovoltaic (PV) panels, the roof-based BIPVT are classified into three classes: cooling of PV, air heating, and water heating. Each class is further divided into several types according to the designs of the integrated PV roofs. Compared with BIPV systems, the total efficiency of most BIPVT systems is significantly improved. However, the decrease in electricity output and adverse impact on the indoor environment is also found for some designs of BIPVT systems in some climates. The advantages and disadvantages of various designs are discussed. Issues to be further studied in the future are also provided in this review. [ABSTRACT FROM AUTHOR]

PV windows are considered to be a promising building-integrated PV technology due to their excellent electrical, daylighting and thermal performance. However, the shading of the PV cells increases the heating load in winter and the high temperature of PV cells exacerbates the cooling load in summer. Moreover, a significant portion of the heat absorbed by the PV modules is usually wasted. To address these issues, this paper proposed a multi-functional PV/T window. This system could recover the heat on PV cells for producing warm air in winter and hot water in other seasons. As a result, the proposed system can realize the combined PV/T utilization of solar energy and reduce the seasonal thermal needs of the building. First, the mathematical model of the proposed window was developed and validated. Next, the developed model was integrated into building energy software to evaluate the overall energy performance of the proposed window in four different cities of Yangtze River region: Shanghai, Nanjing, Wuhan, and Chongqing in China. These cities experience the subtropical humid climate (Cfa in the Köppen-Geiger classification) and belong to the zones with medium global horizontal irradiation. The sensitivity analysis revealed that the optimal slat angles are between 0o and 40o, while the optimal orientation angles range from 30o to 60o. Selecting low-iron glass as the inner glass resulted in the lowest overall energy consumption. By adopting the MFPV/T window, the annual operation cost of the studied case could be reduced by 34% (6.4 $/m2), 36% (7.0 $/m2), 28% (5.7 $/m2), and 16% (2.9 $/m2) in these four cites, respectively. Additionally, the annual CO 2 emissions could be curtailed by 53.5, 58.5, 47, and 22.3 kg/m2, respectively. • A novel PV/T window was proposed for addressing the common issues of PV windows. • The mathematical model of proposed window was developed and validated. • The parameter sensitivity was analyzed. • The overall energy performance was predicted in Yangtze River region. [ABSTRACT FROM AUTHOR]

• A rolling optimization strategy is proposed for microgrid operation optimization. • Flexible load and hybrid energy storage are applied jointly to power regulation. • A priority-based power fluctuations smoothing method is first proposed. • Energy trigger mechanism is designed to keep the optimal state of supercapacitor. The rise of microgrids provides an effective solution to the problem of local consumption of renewable energy sources. However, the power fluctuations are the crucial issue for the widespread adoption of the grid-connected microgrid with renewable energy sources. We aim to economically and locally solve the problem of grid-connected power fluctuations of microgrid. In this paper, a novel rolling optimization strategy considering grid-connected power fluctuations smoothing for microgrids is provided. Firstly, the mathematical model of the microgrid is described, which contains the grid-connected power limits and supercapacitor-battery hybrid energy storage system. Then, a priority-based smoothing method of power fluctuations is proposed for the first time. As a flexible load, the heating/cooling load is used as virtual energy storage to participate in power regulation. Finally, the rolling optimization strategy integrated with the energy trigger mechanism is designed to achieve the operation optimization. The objectives of this paper are to help the microgrids improve grid-connection friendliness and minimize the daily operating costs that include fluctuation penalties. Simulation results on different scenarios for a grid-connected microgrid show that the novel rolling optimization strategy has better performance in any scenario. Compared with the traditional strategy, the total operating costs of the proposed strategy can save 5.67% for a given scenario. We can conclude that the proposed rolling optimization strategy can effectively reduce cost payment and meet the requirement of grid-connected power fluctuations smoothing. [ABSTRACT FROM AUTHOR]

Exterior shading devices and outdoor units can be closely coupled since these two building components are commonly installed next to each other. This study uses a coupled EnergyPlus-Fluent modeling approach to examine how a combination of exterior shading and heat rejection from outdoor units can affect the ambient outdoor environment of a building, and how changes in the ambient outdoor environment can influence cooling loads and indoor PM2.5 exposure. Three exterior shading devices were simulated, including horizontal overhangs, vertical overhangs, and vertical fins. Data from wind-tunnel experiments and field measurements were used to ensure the accuracy of the airflow model, energy model, and pollution model developed in this study. Results indicate that horizontal overhangs could almost offset the increase in cooling loads due to increased ambient outdoor temperatures caused by heat rejection. The use of vertical overhangs did not always mean lower demand for space cooling when heat rejection was considered. Heat rejection, horizontal overhangs, and vertical overhangs could help reduce indoor PM2.5 exposure, while indoor air pollution was worse after the implementation of vertical fins. This study shows how exterior shading devices and outdoor units can be coupled to achieve better building energy efficiency and improved occupant health. [ABSTRACT FROM AUTHOR]

Due to urban warming, the energy demand for cooling buildings is rising. The current study used CitySim (building energy model) to estimate the cooling energy requirements for 40 buildings in downtown Dubai using high-resolution climate data from weather research and forecasting (WRF) coupled with the single layer urban canopy model (SLUCM). Simulating the four mitigation scenarios allowed for the examination of the reduction in cooling load caused by the addition of greenery at a rate ranging from 25% to 100%. The insulated building's cooling demand reduced by a maximum of 13.89% under 100% GI (M4). Scenario M4 resulted in a reduction of 4.6 kWh/m2 and 3.1 kWh/m2 for the non-insulated and insulated low-rise residential buildings, respectively, while the high-rise buildings saw a reduction of 3.09–4.91 kWh/m2 for the non-insulated and 2.07–3.09 kWh/m2 for the insulated buildings. This study offers a potential remedy to deal with the problem of urban heating in subtropical environments. [ABSTRACT FROM AUTHOR]

Here, a new energy‐saving optimization method, known as the Improved Particle Swarm Optimization (IPSO) algorithm, is proposed to solve the practical engineering problem of high energy consumption during the operation of central air‐conditioning water systems. The mathematical model of total energy consumption optimization and the calculation principle of the IPSO are established. It is shown via experiments that the proposed algorithm requires a short optimization time and can achieve the optimal operation conditions of water systems under various cooling loads, where these conditions include the optimal control strategy of equipment, and water temperature at the inlet and outlet of chillers. Furthermore, it ensures the lowest energy consumption of water system operation. The measured results show that the comprehensive energy saving rate of water system operation can reach more than 18% compared with that before the system transformation. [ABSTRACT FROM AUTHOR]

As building systems account for almost half of the total energy consumed by the building sector to provide space heating, cooling, and ventilation, efficiently designing these systems can be the key to energy conservation in buildings. Dual VAV systems with an effective control strategy can substantially reduce the energy consumption in buildings, providing a significant scope of further research on this system configuration. This paper proposes to utilize the warm air duct of the dual VAV system as a dedicated outdoor air (DOA) unit when no heating is required, which allows the cooling load to be effectively distributed between two ducts. A specific control sequence is proposed with different supply air temperature reset strategies to estimate the heating, cooling loads, and fan power energy consumption of the proposed system. A simple two-zone office building is taken as a preliminary case study to simulate the airflow rates and fan power of a single duct VAV and proposed dual VAV systems to illustrate the concept. Finally, a larger multi-zone office building is simulated to measure the annual heating, cooling loads, and fan power energy and compare the energy savings among the systems. The results show significant fan power reduction ranging from 1.7 to 9% and notable heating energy reduction up to 76.5% with a small amount of cooling load reduction varying from 0.76 to 2.56% depending on the different locations for the proposed dual VAV systems. Further energy savings from different supply air temperature reset strategies demonstrate the opportunity of employing them according to climates and case studies. The proposed dual VAV system proves to have the potential to be adapted in buildings for the purpose of sustainability and energy savings. [ABSTRACT FROM AUTHOR]

• Proposes a dual loop Inverter control strategy. • Implementation of the pf and QV droop-based control strategy for power sharing of the DGs in microgrid. • Implementation of inertia based frequency control strategy for high overshoot in frequency transient. • Impementation of priority based load management using deep learning technique. The paper presents a hierarchical voltage and frequency control framework for peak and off-peak hours to enhance grid resiliency. The primary control is equipped with dual loop control mechanisms to maintain voltage and frequency during off-peak hours. Further, it provides additional inertia (virtual inertia) to reduce overshoot present in frequency transient (due to sudden switching of Loads). The secondary control strategy incorporates priority based intelligent management of Thermostatic Control Loads (TCLs) considering Customer Thermal Comfort (CTC). A Deep-learning based aggregation and control of TCLs (heating and cooling) is implemented to mitigate frequency deviation and moderates the overall energy usage during peak hours. Further, priority between the heating and cooling load (to be controlled) is decided with seasonal variation. Coordination between the primary and secondary control mechanism is established to enhance the dynamic response of the system during both peak and off-peak hours. Performance assessment of the proposed strategy is carried out in MATLAB/Simulink platform considering several dynamics. Further, the Hardware in Loop (HIL) is performed on OPAL-RT to showcase the efficacy of the proposed mechanism under realistic conditions. [ABSTRACT FROM AUTHOR]

Energy efficiency in building design tends to be important in reducing the share of construction sector in energy consumption and environmental problems caused by it. In this study, energy performance of an existing office structure was evaluated by integrating shading elements and double skin facade systems within the scope of energy efficiency in buildings. Initially, current energy performance of the building was examined by design builder program and then, the shading element alternatives and the double skin facade system alternatives were separately integrated into the structure and simulated by solar tool and design builder program. Alternatives with the highest energy performances were determined in both building components. Later in the study, the shading element and the double skin facade system alternatives, which gave the most successful results, were integrated into the building model simultaneously and the energy performance thus evaluated was compared with the energy performance of the existing building. According to results, the Shaded Double Skin Facade scenario indicated a drop in heating load of around 2% and a decrease in cooling load of about 45% compared to the present building model. [ABSTRACT FROM AUTHOR]

Buildings sector being responsible for more than one-third of the overall energy consumption is growing rapidly. One of the top priorities of building designers is to minimize energy consumption. This communication presents a multi-framework-based approach to optimize dwelling conditions and energy efficiency in subtropical climatic conditions. In the present work, the thermal behavior of indoor air is assessed by using Ansys Workbench while the Grasshopper plugin of Rhinoceros 3D is used to evaluate the daylighting under different scenarios. The study also involves a survey with a sample size of 289 people to substantiate the various optimization strategies. Further, the work encompasses the validation of respective approaches by comparing the results with measured values. Based on the results, it has been observed that using a ceiling fan and air conditioner alongside during the summer season in subtropical climatic conditions provides better dwelling conditions and reduces cooling load by 20–30% in comparison with an air conditioner alone. While consciously utilizing daylight by ensuring minimal illuminance of 500 lx is having the ability to decrease the lighting load by approximately 27%. Further, the results obtained from the present investigation can serve as a basis for providing better dwelling conditions with optimized energy utilization. [ABSTRACT FROM AUTHOR]

• Micro heat pipe array-based BIPVT module enables efficient heat utilization. • A novel model of the BIPVT module and energy supply system was established. • Highly efficient combination of BIPVT and heat pump utilization is achieved. • Summarized the energy-saving features of BIPVT building in different seasons. • The novel BIPVT buildings achieve nearly zero energy standards. Building integrated photovoltaic–thermal (BIPVT) technology is an efficient form of solar energy utilization, which realizes self-energy supply and holds good development prospects. In this paper, a novel BIPVT module based on micro heat pipe array and BIPVT near-zero energy building are proposed. A matching multi-energy complementary energy supply system is also proposed for this building. The building and energy supply system models were built using TRNSYS. The load characteristics of BIPVT buildings were studied by analyzing the heat transfer process of BIPVT components. The energy-saving rate of the building was also calculated using the method of near-zero energy building evaluation index. The accumulated cooling load and heating load of the buildings throughout the year show that those of the BIPVT buildings are 8.6% higher than those of the benchmark building. Comparison of the energy consumption results shows that the annual cumulative energy consumption of the BIPVT building is 32.3% lower compared to the benchmark building's. In addition, 61.2% of the energy required for year-round refrigeration, heating, and electricity can be supplied by this system. The energy consumption index of the BIPVT building is 70.26 kWh / (m2 • a). Compared with the benchmark building, the BIPVT building energy saving rate is 76.6% and the building energy efficiency improvement rate is 32.3%. [ABSTRACT FROM AUTHOR]

Climate change is one of the main problems that humanity is currently facing due to carbon dioxide emissions caused by fossil fuel consumption. Organic Rankine cycles may play an important role in reducing these emissions since they can use industrial waste heat or renewable energies. This study presents the proposal and modeling of an organic Rankine cycle integrated into a double-effect absorption cooling system for the simultaneous production of power and cooling. The working fluids utilized were the ammonia–lithium nitrate mixture for the absorption system and benzene, cyclohexane, methanol, and toluene for the organic Rankine cycle. The influence of the primary operating parameters on the system performance was analyzed and discussed in terms of cooling load, turbine power, energy utilization factor, and exergy efficiency for a wide range of operating conditions. It was found that, for all cases, the cooling load was dominant over the turbine power since the minimum cooling load obtained was above 50 kW, while the maximum turbine power was under 12.8 kW. For all the operative conditions analyzed, the highest performance parameters were obtained for benzene, achieving an energy utilization factor of 0.854 and an exergy efficiency as high as 0.3982. [ABSTRACT FROM AUTHOR]

Global warming and the urban heat island effect have led to the deterioration of the outdoor thermal environment and the rise of building cooling demand, calling for urban design with high environmental quality. However, little has been changed in the practice of urban street design. This paper bridges this gap by developing a framework of parametric simulation for design guidance of urban streets with an advanced urban canopy model that can simulate the interactive indoor-outdoor environment. Case studies are conducted at the neighborhood scale for six cities to improve outdoor thermal comfort and building energy efficiency in summer. The influence of street and building design parameters has been investigated based on 31104 simulations. Results reveal the conflict between optimizing outdoor thermal comfort and optimizing building energy efficiency. In the majority of the investigated cities, window-to-wall ratio, window type, and street orientation are the most influential parameters. Strategies by adjusting these parameters can significantly improve outdoor thermal comfort, as quantified by thermal stress hours, at a cost of increasing the building cooling load. Planting bigger and denser roadside trees always moderately reduces the thermal stress hours and building cooling load. These results demonstrate the potential of this framework to identify the design trade-offs between thermal comfort and building energy efficiency. The framework can be extended to various cities to help designers and policymakers at the early design stage. • A framework to explore early design guidance for urban streets is developed. • A total of 31104 design scenarios are performed for six cities in southern China. • Window-to-wall ratio, window type, and street orientation are most influential factors. • Pareto optimal solutions for outdoor thermal comfort and building energy are revealed. [ABSTRACT FROM AUTHOR]

Many models have been developed to predict the energy consumption of various building types, including residential, office, institutional, educational, and commercial buildings. However, to date, no models have been designed specifically to predict poultry buildings' energy consumption. To address this information gap, this study integrated data-driven techniques, including artificial neural networks (ANN), support vector regressions (SVR), and random forest (RF), into a physical model to predict the energy consumption of poultry buildings in different climatic zones in Turkey. The following statistical indices were employed to evaluate the model's effectiveness: Root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The calculated and predicted values of the heating and cooling loads were also compared using visualization techniques. The results indicated that the RF model was the most accurate during the testing period according to the RMSE (0.695 and 6.514 kWh), MAPE (3.328 and 2.624%), and R2 (0.990 and 0.996) indices for heating and cooling loads, respectively. Overall, this model offers a simple decision-support tool to estimate the energy requirements of different buildings and weather conditions. [ABSTRACT FROM AUTHOR]

In hot dry regions, the building envelope receives abundant solar radiation, which contributes to heat stress and indoor thermal discomfort. To mitigate overheating inside spaces, cooling is the main basic requirement during most of the year. However, due to the harsh climatic conditions, buildings fail to provide passively the required comfort conditions. Consequently, they are fully dependent on-air conditioning systems, which are huge energy consumers. As roofs are exposed to the sun throughout the daytime, they are estimated to be the main source of heat stress. In return, they can contribute significantly to achieve optimum comfort and energy savings when efficient design strategies are used in an early design stage. To examine the potential for cooling load reduction and thermal comfort enhancement by using cooling roof techniques in residential buildings, a study was performed in the city of Biskra (southern Algeria). Accordingly, an in-field measurement campaign was carried out on test-cells during five days in summer. Three different cooling roof techniques were addressed: (a) cool reflective white paint (CR), (b) white ceramic tiles (CT) and (c) a cool-ventilated roof (C-VR). These roofing alternatives were investigated by monitoring both roof surface temperatures and indoor temperatures. Comparative analysis showed that a cool-ventilated roof is the most efficient solution, reducing the average indoor temperature by 4.95 °C. A dynamic simulation study was also performed based on TRNSYS software to determine the best roofing system alternatives in terms of thermal comfort and energy consumption, considering the hottest month of the year. Simulation tests were run on a base-case model representing the common individual residential buildings in Biskra. Results showed that a double-skin roof combined with cool-reflective paint is the most efficient roofing solution. By comparison to a conventional flat roof, meaningful improvements have been achieved, including reducing thermal discomfort hours by 45.29% and lowering cooling loads from 1121.91 kWh to 741.09 kWh. [ABSTRACT FROM AUTHOR]

In this article, the consumption of energy in Internet-of-things-based smart buildings is investigated. The main goal of this work is to predict cooling and heating loads as the parameters that impact the amount of energy consumption in smart buildings, some of which have the property of symmetry. For this purpose, it proposes novel machine learning models that were built by using the tri-layered neural network (TNN) and maximum relevance minimum redundancy (MRMR) algorithms. Each feature related to buildings was investigated in terms of skewness to determine whether their distributions are symmetric or asymmetric. The best features were determined as the essential parameters for energy consumption. The results of this study show that the properties of relative compactness and glazing area have the most impact on energy consumption in the buildings, while orientation and glazing area distribution are less correlated with the output variables. In addition, the best mean absolute error (MAE) was calculated as 0.28993 for heating load (kWh/m2) prediction and 0.53527 for cooling load (kWh/m2) prediction, respectively. The experimental results showed that our method outperformed the state-of-the-art methods on the same dataset. [ABSTRACT FROM AUTHOR]

• Addressed a real-world challenge for load-shifting multiple ON/OFF staged units. • Presented a unique MPC that shifts load while eliminating undesired peak. • Presented comprehensive 2-month experimental MPC demonstration results. • Achieved significant load shifting with over 30% demand savings. • Discussed experimental charging and discharging processes of building thermal mass. Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. Two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented. [ABSTRACT FROM AUTHOR]