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In this paper, a cascade refrigeration system equipped with a single-screw compressor (SSC-CRS) is proposed and built in order to achieve the goal of high load cooling capacity under ultra-low temperature conditions. Firstly, the isentropic efficiency curve of the single-screw compressor (SSC) is fitted through experimental method in response to the characteristics of low suction temperature and large pressure ratio of the compressor. In addition, the analysis of SSC-CRS with 28 refrigerant pairs are made to compare the coefficient of refrigeration (COP) and power consumption. On the basis of the cascade system, the paper compares the improvement of performance between two circuits with SSC: vapor injection cascade refrigeration system (SSC-ICRS) and ejector vapor injection cascade refrigeration system (SSC-EICRS). The results indicate the COP improvement rate of SSC-ICRS system is 17.39 %–41.98 % and the COP improvement rate of SSC-EICRS system is 49.46 %–68.37 % compared with SSC-CRS. • The isentropic efficiency curve of the single-screw compressor is fitted through experimental method. • The analysis of SSC-CRS with 28 refrigerant pairs are made to compare the coefficient of refrigeration and power consumption. • The COP improvement rate of SSC-EICRS system is 49.46 %–68.37 % compared with SSC-CRS. [ABSTRACT FROM AUTHOR]

More accurate multivariate load forecasting is conducive to integrated energy system planning optimization and economic operation. In this paper, based on the consideration of exogenous factors such as weather, endogenous factors are added into the prediction, and a short-term prediction model for integrated energy multivariate load based on endo-exogenous uncertainty is established using fine-tuned transfer learning. The LSTM pre-training model is first initially established using genetic algorithm and exogenous factors such as weather. Then the exogenous uncertainty of the pre-training model and the knowledge of endogenous uncertainty in the target model are combined using fine-tuned transfer learning to continuously optimize the basic parameters of the prediction model to obtain the final multivariate load prediction model. The performance of the four models is comprehensively evaluated after the historical load validation of the integrated energy system in Beijing, China. Compared with the traditional LSTM multivariate load forecasting model, the MAPE of the proposed model in this paper is reduced by 21.03 %, 13.00 %, and 24.39 % for electricity, heat, and cooling load forecasting, respectively. • The coupling mechanism between multiple energy sources of the integrated energy system is analyzed. • An endogenous-exogenous uncertainty method is proposed to extract the dynamic coupling relationship between multiple loads. • An endogenous-exogenous uncertainty-based GA-LSTM multivariate load prediction model is developed. • Optimizing model parameters using fine-tuned migration learning to deepen the impact of endo-exogenous uncertainty. [ABSTRACT FROM AUTHOR]

In this study, optimal discharge conditions of sorption thermal battery (STB) are determined for stable supply of the building cooling load based on different load patterns. Parametric analysis is conducted to ascertain the influence of heat source temperature and the mass flow rate ratio between the solution and the heat transfer fluid on the performance of the STB. The maximum values of energy storage density (ESD) and coefficient of performance (COP) are estimated as 207.73 kWh/m3 and 0.74, respectively. The daily transient behaviors of STB are examined in detail, and the optimal solution charge and the solution flow rate are quantified based on the building types (residential, hospital, hotel, and office). For a building with an air conditioning floor area of 181.63 m2, the most suitable cooling load response is observed in the hotel scenario. The minimum deviated cooling output from the building load is 8.69%. Concurrently, COP and ESD are estimated of 0.69 and 101.99 kWh/m3. This paper demonstrates the cooling load responsiveness and optimal operating point of STB based on building load pattern. Additionally, a dimensionless number is defined to generate the discharge trend and enable to predict COP and ESD. Optimzed sorption thermal battery for responding the building cooling demand. [Display omitted] • Sorption thermal battery is optimized for overnight cooling supply. • Feasibility study of four types of building cooling load pattern is conducted. • 207.73 kWh/m3 of thermal energy is stored and coefficient of performance is 0.74 • Hotel scenario shows the superior feasibility by matching 91.3% of demand. [ABSTRACT FROM AUTHOR]

Abstract Neighbourhood form imposes complex effects on the local microclimate. The overlook of neighbourhood microclimate will lead to miscalculation of building energy performance. In this paper, a neighbourhood-scale building simulation coupled between building energy simulation and CFD is proposed to assess the impact of external convective heat transfer coefficient (CHTC) to the building thermal performance. The co-simulation is run for different types of buildings inside the neighbourhood with different combinations of neighbourhood form parameters. The developed simulation scheme allows the iterations of neighbourhood design parameters and simulation model generation in CFD and EnergyPlus. Five empirical methods are used to predict CHTC, and the results of CHTC values, heating and cooling load intensity are compared in detail with the proposed CFD coupled method. The results suggest that the orientation and multiplier are crucial to the building load intensity in Chicago and the neighbourhood form can affect the thermal load of residential building up to about +27.1% and −18.6%, and +17.2% and −7.7% for office buildings. The proposed method and framework in this paper is capable of generating design guidelines for the optimal energy performance of the buildings in the neighbourhood. Highlights • Neighbourhood-scale building block energy simulation coupled with CFD. • Iterated various neighbourhood design parameters and building model generation. • Empirical methods in evaluating CHTCs are evaluated and compared with CFD method. • Neighbourhood form can affect certain type of building's energy use up to ± 20%. • Neighbourhood design guideline for optimal building thermal load is suggested. [ABSTRACT FROM AUTHOR]

To reduce building energy consumption and improve indoor air quality, this paper proposes a CdTe PV double-skin ventilated facade integrated with a thermal-catalytic air type PV/T (PV-DSF-TCPV/T). The mathematical model of the proposed system was developed and validated against the experimental data. The daily and seasonal overall performances of the proposed system, including the electrical, thermal, and formaldehyde degradation performances, were studied and compared with that of a conventional PV-DSF. Moreover, parametric analysis was conducted. The main results are: (1) The annual electricity outputs of the proposed system and the conventional system were 414.88 kWh and 105.88 kWh, respectively. (2) Compared with the conventional PV-DSF, the heating and cooling loads of the proposed system decreased by 10.85 % and 34.86 %, respectively, due to the reduction of indoor heat gain in summers and the increase of it in winters. (3) The daily formaldehyde conversion ratio and CADR were 30.84 % and 67.86 m3/h (4) The volumetric flow rate and the cell coverage of the PV-DSF both exerted obvious impacts on the proposed system. • A PV-DSF integrated with the thermal catalytic PV/T was proposed. • Corresponding mathematical models were established and validated. • Daily and seasonal energy performances were investigated and compared. • The impacts of two design parameters were analyzed and discussed. [ABSTRACT FROM AUTHOR]

Integration of renewable and waste energy sources, decentralisation, electrification and energy storage, are key pillars of energy transition in the district heating sector. Such a strategy can be implemented at the building level using the multi-carrier energy hub concept, which has recently gained popularity. This paper presents a technical and financial feasibility study of a building-integrated multi-carrier energy hub concept. The case study is performed for an existing large medical centre, where demand occurs simultaneously for electricity, cold, and heat at different temperature levels, including hot process water and steam. The central idea is to replace the high-exergy steam with heat from the municipal district heating network. The concept also includes the integration of heating and cooling loads, heat storage and implementation of an on-site photovoltaic (PV) power plant. The key components of the proposed hub technological structure are high-temperature heat pumps. Five alternative structures of the system configuration are examined using an hour-by-hour annual simulation. The results reveal that the proposed multi-carrier energy hub is fully operational. The project is also profitable, which results mainly from significant energy savings compared to the existing heat supply system using high-pressure steam. However, the profitability strongly depends on the system configuration, and the choice of the best option is ambiguous. The system with heat storage reaches the highest net present value (NPV) while the one without the storage results in the highest internal rate of return (IRR) due to significantly lower capital expenditures (CAPEX). In all cases, the project leads to a considerable cumulative CO 2 emissions reduction, which is at the level of 1351 up to 2961 tons annually depending on system configuration. • Multi-carrier energy hub concept is proposed for a large medical centre. • Heating loads are integrated with district heating network using high-temperature heat pumps. • Cooling and heating loads are integrated using high-temperature heat pumps. • Hour-by-hour simulation of the system including photovoltaic installation is performed. • Technical and financial feasibility of the energy hub system is demonstrated. [ABSTRACT FROM AUTHOR]

In this paper four different air conditioning systems designed for a building located in Naples, are investigated. The first plant is based on distributed energy conversion systems: twelve boilers and twelve chillers. In the second solution the space heating demand is satisfied by a centralised boiler while the cooling load is met by a centralised chiller. The third option consists of a trigeneration plant. The fourth configuration is composed by an electric heat pump satisfying space heating and cooling and a centralised boiler used for domestic needs. Dynamic simulations have been carried out to compare the energy and environmental performance of the centralised systems with those achieved by the distributed one. Besides, as further loads, for the third option, two electric vehicles charged by the cogenerated electricity surplus are compared to diesel cars. The results show that the best energy and environmental performance are achieved by the cogenerator-based system, with a primary energy demand of 120.6 MWh/y and equivalent dioxide carbon emissions of 25.2 t/y in the configuration without vehicles. This result is also confirmed with electric and diesel vehicles. The energy competitiveness of the plant with the electric heat pump is achieved only in a scenario with a high penetration of renewable energy sources. • Autonomous and centralised air conditioning systems are compared. • Dynamic simulations are performed to carry out energy and environmental analyses. • A multi-purpose block of flats is considered as users simulated. • Electric and diesel vehicles are introduced in the analysis. • A sensitivity analysis is carried out considering the renewable energy sources contribution. [ABSTRACT FROM AUTHOR]

Abstract In the mobility sector Electric Vehicles represent one of the main opportunities to ensure strong reduction of local pollution. However, their higher costs compared to gas-fuelled cars are still a barrier for their large diffusions. One possible solutions to increase EVs penetration is their use as storage within households equipped with Renewable Energy Sources enabling a flexible energy management, for instance by the Vehicle-to-Grid and/or Vehicle-to-Home scheme. The aim of this paper is to investigate possible management of the EV battery through an optimization approach capable to minimize the electricity supply costs for an Italian residential end-user with PV, considering battery constraints, such as driving habits. A statistical approach of driver behavior is integrated within the optimization approach to define some possible daily driving patterns. The optimization takes into consideration mobility needs, prices for selling and purchasing electricity and hourly electrical, heating and cooling load profiles of the household. On the basis of these constraints the optimizer identifies when PV overproduction can be either used to charge batteries or to partially cover the load demand of the household. Finally, economic and energy evaluations are performed under Monte Carlo simulations to highlight reliability of potential benefits for the household case study. Highlights • Optimal scheduling of EV battery to reduce electricity supply cost of households. • Monte Carlo simulation for considering statistical driver's habits and mobility needs. • Increase of PV self-consumption through Vehicle-to-Home operation. • Reduction of electricity supply cost by means of V2G and V2H operation. [ABSTRACT FROM AUTHOR]

Abstract Optic-Variable Wall (OVW) holds the property of tunable reflectivity to solar radiation. By allowing more solar radiation absorption in winter while maintaining high reflection in summer, OVW can help energy-saving during the HVAC of buildings. In this paper, the energy saving potential with OVW for stable air temperature control is investigated. Annual meteorological conditions are adopted in the comparison of cooling and heating loads. The effects of properties of OVW and Indoor Air Temperature (IAT) are discussed. It is concluded that OVW is able to simultaneously reduce heating and cooling loads. The optimum onset temperature of OVW is half the temperature zone lower than IAT. With the adoption of OVW, the heating load is close to the case of OFW (Optic-Fixed Wall) with low reflectivity; the cooling load of OVW provides close effect as using high-reflectivity OFW. In terms of tuning temperature, narrow temperature zone is favorable for the reduction of HVAC load. Comparing with OFW, the relative reduction of HVAC load with OVW is independent of conductive thermal resistance, corresponding to the thickness of insulation layer. The results offer new choices for the passive design of green buildings and show directions for future development and performance improvement of OVW. Highlights • HVAC load (heating & cooling) is reduced with OVW for stable air temperature control. • Optimum onset temperature of optic-variation is half zone lower than IAT. • Narrow temperature zone for optic-variation is favorable for the reduction of HVAC load. • OVW offers more significant HVAC load reduction in the case of weak thermal resistance. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the network expansion planning of an active microgrid that utilizes Distributed Energy Resources (DERs). The microgrid uses Combined Cooling, Heating and Power (CCHP) systems with their heating and cooling network. The proposed method uses a bi-level iterative optimization algorithm for optimal expansion and operational planning of the microgrid that consists of different zones, and each zone can transact electricity with the upward utility. The transaction of electricity with the upward utility can be performed based on demand response programs that consist of the time-of-use program and/or direct load control. DERs are CHPs, small wind turbines, photovoltaic systems, electric and cooling storage, gas fired boilers and absorption and compression chillers are used to supply different zones' electrical, heating, and cooling loads. The proposed model minimizes the system's investment, operation, interruption and environmental costs; meanwhile, it maximizes electricity export revenues and the reliability of the system. The proposed method is applied to a real building complex and five different scenarios are considered to evaluate the impact of different energy supply configurations and operational paradigm on the investment and operational costs. The effectiveness of the introduced algorithm has been assessed. The implementation of the proposed algorithm reduces the aggregated investment and operational costs of the test system in about 54.7% with respect to the custom expansion planning method. Highlights • A new distributed energy resource and network expansion planning algorithm of an active microgrid is proposed. • The proposed method uses a bi-level iterative optimization algorithm for optimal expansion and operational planning. • The transaction of electricity with upward utility can be performed based on demand response programs. • The proposed method is applied to a building complex and five different scenarios are considered. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel approach to temperature probability density distribution and function. Probability density functions and frequency are successfully used in wind speed and solar energy analyses in literature. This study applies these data to temperature data analysis. The present model is developed using the indoor and outdoor temperature as a parameter. Outdoor temperature distribution is crucial for the calculation of monthly and total degree-hour. In this paper, using past weather data, the outdoor temperature probability density functions are modeled for four cities in different regions in Turkey via a new computer program. The main advantage of this approach is to allow us to determine heating and cooling loads with respect to different indoor and outdoor temperatures. [Copyright &y& Elsevier]

Due to the high energy consumption in buildings, cooling load forecasting plays a crucial role in the planning, control and operation of heating, ventilating and air-conditioning systems. However, cooling load data series always exhibit nonlinear and dynamic features, making it very difficult to be forecasted accurately. Therefore, a novel deep learning based hybrid approach is originally proposed in this paper for deterministic cooling load forecasting with high accuracy. The approach is a hybrid of empirical mode decomposition, deep belief network and ensemble technique. Empirical mode decomposition is applied to decompose the original cooling load data series into several components with better outliers and behaviors. The hidden nonlinear features and high-level invariant structures in data are effectively extracted by layer-wise pre-training based deep belief network. In addition, ensemble technique is introduced and properly designed to mitigate the impact of uncertainties, i.e., model uncertainty and data noise, on forecasting accuracy. Case studies using real cooling load data collected from Shenzhen and Hong Kong have been implemented. The numerical results demonstrate that the proposed forecasting approach exhibits competitive performance when compared to the prediction algorithms of the state of the art. It is therefore convinced that the proposed approach has a high potential for improving the operating performance in energy systems. [ABSTRACT FROM AUTHOR]

Cooling system consumes more than 35% of total electricity in most data centers. The provided cooling normally exceeds the actual demand of IT equipment in order to assure the safe operation, resulting in a low energy efficiency. In this paper, a novel method based on demand response was proposed to precisely control the cooling supply, and the energy saving potential was assessed systematically. Compared to the reference case, in which the cooling demand is determined by assuming all of servers are in the running status, when the cooling demand was determined based on the measured dynamic IT load at room level, row level, rack level and server level, it can be reduced by 7.9%, 14.2%, 15.6% and 17.9% respectively for the random selected 48 h. In addition, IT load shifting also has a big potential to save energy, as it can make the cooling system working at a higher energy efficiency, which varies with loads. Two cases were studied: even distribution of IT load and optimized IT load shifting. Compared to the best case that determines the cooling demand according to the IT load at server level, they can further reduce the electricity consumption of cooling systems by 0.9%, and 1.2%. [ABSTRACT FROM AUTHOR]

The integrated use of electricity and natural gas has captured great attention over recent years, mainly due to the high efficiency and economic considerations. According to the energy hub design and operation, which allows using different energy carriers, it has turned into a critical topic. This paper develops a two-stage stochastic model for energy hub planning and operation. The uncertainties of the problem have arisen from the electric, heating, and cooling load demand forecasts, besides the intermittent output of the solar photovoltaic (PV) system. The scenarios of the uncertain parameters are generated using the Monte-Carlo simulation (MCS), and the backward scenario reduction technique is used to alleviate the number of generated scenarios. Furthermore, this paper investigates the effectiveness of demand response programs (DRPs). The presented model includes two stages, where at the first stage, the optimal energy hub design is carried out utilizing the particle swarm optimization (PSO) algorithm. In this respect, the capacity of the candidate assets has been considered continuous, enabling the planning entity to precisely design the energy hub. The problem of the optimal energy hub operation is introduced at the second stage of the model formulated as mixed-integer non-linear programming (MINLP). The proposed framework is simulated using a typical energy hub to verify its effectiveness and efficiency. • Proposing an MINLP model for the optimal energy hub design and operation. • Modeling the seasonal variations of loads demand for each season. • Evaluating the impact of DRPs on the sizing of the energy hub assets. • Investigating the effect of uncertainties on the optimal energy hub design. • Investigating a comprehensive energy flow model for serving the loads. [ABSTRACT FROM AUTHOR]

This paper presents a multi-step-ahead forecasting method of the electric demand in a large institutional building to be used in the context of demand response control strategy. A cascade-based method is proposed for electric demand forecasting of the cooling system over the next six hours with a time-step of 15 min. Data mining techniques are used for pre-processing the measurements and improving the forecasting models. Data-driven models are developed by using Building Automation System (BAS) trend data of an existing building. First, the air flow rate supplied by the Air Handling Units (AHUs) is forecasted, followed by the cooling coils load, and the whole building cooling load. Finally, the electric demand of the supply fans, chillers and cooling towers, and the total electric demand of the cooling system of the building are forecasted over six hours. The comparison of the forecasted electric demand of the cooling system for the existing building over the six-hour test and the measurements show good agreement with CV(RMSE) of 14.2–22.5%. [ABSTRACT FROM AUTHOR]

A methodology is proposed for on-line monitoring of cooling load supplied by Telecommunication (TLC) cooling systems. Sensible cooling load is estimated via a proportional integral controller-based input estimator, whereas a lumped parameters model was developed aiming at estimating air handling units (AHUs) latent heat load removal. The joint deployment of above estimators enables accurate prediction of total cooling load, as well as of related AHUs and free-coolers energy performance. The procedure was then proven effective when extended to cooling systems having a centralized chiller, through model-based estimation of a key performance metric, such as the energy efficiency ratio. The results and experimental validation presented throughout the paper confirm the suitability of the proposed procedure as a reliable and effective energy monitoring and diagnostic tool for TLC applications. Moreover, the proposed modeling approach, beyond its direct contribution towards smart use and conservation of energy, can be fruitfully deployed as a virtual sensor of removed heat load into a variety of residential and industrial applications. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization design methodology for borehole heat exchangers coupled with heat pump units by controlling maximum/minimum inlet fluid temperature in the life cycle of ground coupled heat pump system. Base on the hourly performance simulation of ground coupled heat pump system, the optimal combination of the distance between boreholes, borehole depth and borehole number under a given annual cooling/heating load is determined using the Hooke-Jeeves pattern search algorithm. A case study is presented to validate the effectiveness and feasibility of the proposed method for optimal design of borehole heat exchangers. When minimum inlet fluid temperature is decreased from 5 °C to 0 °C, total borehole lengths and total areas required for the installation of single U-pipe borehole heat exchangers are reduced by 13.3% and 75.4% respectively, for double U-pipes borehole heat exchangers, the corresponding reductions are 11.5% and 73.1% respectively. For the same minimum inlet fluid temperature (0 °C), the minimum reduction of total areas required for borehole heat exchangers installation is still up to 21% when the type of borehole heat exchangers is changed from single U-pipe to double U-pipes. [ABSTRACT FROM AUTHOR]

Energy consumption by cooling and lighting loads can be significantly reduced if real time information on the occupancy is applied in their control. To meet this goal smart cooling and lighting devices, which have built-in sensors and controllers and which make decisions on individual basis, are being developed. However, it appears that no attempt has yet been taken for auto-operation of the huge number of already existent so called "non smart" air conditioning (AC) and lighting loads considering the count and location of occupants. A good deal of research has been reported in literature on the line of deploying or improving occupant detecting sensors/cameras, processing the captured images and developing high performance occupant identification (only detection or detection and counting) algorithms. Those were focused on locating users and surveillance in a building. A comparison of those works reveals that cameras combined with faster RCNN (Region based Convolutional Neural Network) algorithm can best identify the occupants. However, none of the works reported any prototyping considering occupancy with or without other ambient conditions such as comfort level, temperature, humidity and luminance for cooling load set point temperature update and/or lighting load control. Furthermore, the way the existing cooling and lighting loads can be made amenable to occupancy sensitive operation for energy saving in a noninvasive way is not yet reported. The novelties and contributions of the present paper are on bridging the gaps viz. (i) prototyping using traditional cooling and lighting loads of any brand already existent in a space for achieving occupancy sensitive energy saving, (ii) developing a model using the total count of occupants in a space along with ambient temperature and humidity for update of the set point temperature of cooling loads such that energy will be saved while the occupants will feel comfort, (iii) generation of ON/OFF commands in real time for lighting loads considering zone wise location of occupants along with the difference between ambient and required luminance in a zone, (iv) auto transmission of the updated set point temperature and on/off commands wirelessly from a common GUI (Graphical User Interface) respectively to the cooling and lighting loads without requiring any "hand held remote", and (v) noninvasive implementation i.e. no need to modify or open the existing cooling and lighting loads at all and fixing any gadget inside these. The method proposed has been implemented on 3 traditional split type AC units of different brands and 16 composite units of LED (Light Emitting Diode) tube lights already existent in a lab. Compared with the manual mode of control which is insensitive to occupancy, the proposed method shows an energy saving in the range of 12.7%–36.15% for cooling loads while in the range of 35%–87.5% for lighting loads as the occupancy varies from high to low. The main economic and commercial impact of the present research is avoiding or postponing the replacement of the huge number of existing cooling and lighting devices by their smart counterparts which is often unaffordable for the entities in many countries. • Occupancy sensitive energy saving for non-smart cooling and lighting loads. • Brand independent and cyber threat free control of cooling and lighting loads. • Noninvasive method requiring no opening or modification of existing loads. • 12.7%–36.15% energy saving for cooling loads while 35%–87.5% for lighting loads. • Avoiding the replacement of existing cooling and lighting loads by smart counterparts. [ABSTRACT FROM AUTHOR]

Using simulated load obtained by energy consumption simulation software is a feasible way to optimize operation of combined cooling heating and power (CCHP) system. However, due to attenuation and delay of thermal energy on transmission, there may be a mismatch between real demand and supplied energy. To obtain more accurate assessment of supplied energy, this paper analyzes characteristics of thermal load through correlation analysis and principal component analysis. Then, the model of thermal load forecasting considering attenuation and delay on transmission is constructed and a case study with actual monitored data in an energy system is employed. Finally, a method of feed-forward active operation optimization for CCHP system is put forward. Based on forecasted thermal load on weekday and weekend in heating, transition and cooling season, dynamic matching optimization and evaluation were carried out. Results show that mean absolute percentage error are 5.43% for heating load forecasting and 6.84% for cooling load forecasting, respectively. The performances of CCHP system are better than that of separate system based on the forecasted thermal load and the maximum integrated performance index is 68.81%, obtained at weekend in cooling season. • Selection and dimension reduction of input variable are studied for load forecasting. • Thermal load forecasting is conducted with actual monitored data. • A method of feed-forward active operation optimization for CCHP system is put forward. • Dynamic optimization results at weekday and weekend are illustrated, respectively. [ABSTRACT FROM AUTHOR]

Uncertainties exist widely at the planning and design stages of district cooling systems, which have significant impacts on the design optimization. This paper therefore proposes a design method for district cooling systems by quantifying the uncertainties, which is so-called uncertainty-based design optimization method. Uncertainties in the outdoor weather, building design/construction and indoor conditions are considered. The application of the uncertainty-based design optimization method is examined in several aspects: the performance assessment, system sizing, configuration selection and technology integration. With the performance distribution at different risk levels, the design of district cooling systems can be determined by the stakeholders based on the compromise between quantified risk and benefit. Sensitivity analysis is conducted to identify influential variables with uncertainties for the cooling loads of district cooling systems. Results show that the uncertainties in the indoor condition are the most important and the uncertainties in building design/construction have the least impact. [ABSTRACT FROM AUTHOR]

Driven by advances in smart grid technologies, building prosumers have closer interaction in sharing energy and information towards net-zero energy communities with energy flexibility and smart readiness. Many studies have focused on transactive energy systems, whereas limited attention was paid to coordinated prosumer management involving electric and electro-thermal energy storages. In this paper, a hierarchical energy management system was developed for a commercial neighbourhood, where distributed prosumers can share their generation and batteries, and manage cooling loads and thermal storages, in subtropical Hong Kong. Several operational scenarios were formulated considering building-level and neighbourhood-level controls, to explore the synergies through peer-to-peer (P2P) sharing and demand flexibility. Techno-economic-environmental impacts were analysed comprehensively from the perspectives of the network, the utility, and each participant. Results show that by integrating the demand-side flexibility, the P2P system could use electricity more effectively and economically. The formulated P2P operation strategy utilising neighbourhood-level control minimised the operational cost by 24.6% and net equivalent CO 2 emission by 7.1%, compared to the traditional peer-to-grid operation. The profit for each participant can be allocated by regulating the compensating price in the dynamic internal trading. The study provides prosumers energy-flexible and grid-friendly operation to facilitate the sustainability of the smart grid. • Hierarchical energy management for active prosumers with hybrid energy storages. • Performance comparison under individual and coordinated demand-side controls. • Enhancing local self-sufficiency by 22.8% via the power and information sharing. • Analysis of the participants' cost savings with a dynamic internal trading model. • Guidelines for system planners and operators to promote low-carbon communities. [ABSTRACT FROM AUTHOR]

The application of PV double skin ventilated windows (PV-DSV) can reduce intense sunlight from entering room and meantime lower the cell temperature. Vacuum glazing is superior in thermal insulation. To further strengthen the thermal insulation of the PV-DSV, this paper proposes an integrated vacuum glazing with PV double-skin ventilated window (VPV-DSV), which employs the CdTe PV glazing and the vacuum glazing as the front and back glazings. Thermal and electrical performance of the VPV-DSV was investigated and compared with the PV vacuum insulating glazing unit (VPV-IGU) through experiment and simulation. Comparison of PV-DSV with different types of back glazing was made to explore the influence of back glazing types in PV-DSV. Furthermore, influence of the window design factors was explored. Results indicated that the use of vacuum glazing improved the thermal insulation of PV-DSV and resulted in 59.8% reduction of the U-value. VPV-DSV reduced more cooling load, but VPV-IGU outperformed VPV-DSV in thermal insulation. Owing to a small temperature coefficient of the CdTe cell, their power output was similar. In addition, the WWR and PV coverage had more obvious impacts on the performance of VPV-DSV than the emissivity of low-e coating. • CdTe glazing and vacuum glazing were integrated in ventilated window. • PV vacuum ventilated window were compared with other two windows. • Mathematical models of these windows were developed and validated. • Impacts of back glazing type of ventilated windows was discussed. • Design factors of the PV vacuum ventilated window were analyzed. [ABSTRACT FROM AUTHOR]

The application of PV double skin ventilated windows (PV-DSV) can reduce intense sunlight from entering room and meantime lower the cell temperature. Vacuum glazing is superior in thermal insulation. To further strengthen the thermal insulation of the PV-DSV, this paper proposes an integrated vacuum glazing with PV double-skin ventilated window (VPV-DSV), which employs the CdTe PV glazing and the vacuum glazing as the front and back glazings. Thermal and electrical performance of the VPV-DSV was investigated and compared with the PV vacuum insulating glazing unit (VPV-IGU) through experiment and simulation. Comparison of PV-DSV with different types of back glazing was made to explore the influence of back glazing types in PV-DSV. Furthermore, influence of the window design factors was explored. Results indicated that the use of vacuum glazing improved the thermal insulation of PV-DSV and resulted in 59.8% reduction of the U-value. VPV-DSV reduced more cooling load, but VPV-IGU outperformed VPV-DSV in thermal insulation. Owing to a small temperature coefficient of the CdTe cell, their power output was similar. In addition, the WWR and PV coverage had more obvious impacts on the performance of VPV-DSV than the emissivity of low-e coating. • CdTe glazing and vacuum glazing were integrated in ventilated window. • PV vacuum ventilated window were compared with other two windows. • Mathematical models of these windows were developed and validated. • Impacts of back glazing type of ventilated windows was discussed. • Design factors of the PV vacuum ventilated window were analyzed. [ABSTRACT FROM AUTHOR]

Since cooling and heating loads are regarded as significant parameters to examine the energy performance of buildings, the need to predict and analyze them for the residential buildings seems to be undeniable. Hence, the present paper wants to optimize the multi-layer perceptron neural network using several optimization methods to predict the heating and cooling of energy-efficient buildings. The dataset used in this study consists of eight independent factors: surface area, wall area, roof area, relative compactness, overall height, orientation, glazing area, and glazing area distribution. To prove the reliability and accuracy of the obtained results, test and training data are also considered. According to the statistical results, biogeography-based optimization has the highest value of R2 and the lowest values of RMSD, normalized RMSD, and MAE in both training data and test data for cooling and heating loads. Hence, the forecasting accuracy of the proposed MLP neural network optimized with the BBO optimization algorithm with the RMSD, R2, and MAE of 2.82, 0.920, 2.15 in the training phase of heating load and with the RMSD, R2, and MAE of 3.18, 0.887, 2.97 in the training phase of the cooling load is much better than those of the other models. • The effect of eight common building parameters on heating and cooling load is studied. • MLP neural network is used to predict the heating and cooling loads of a residential building. • Through different optimization algorithms, optimal parameters of the MLP model are determined. • Biogeography-based optimization outperforms other optimization algorithms in improving neural network. • A good agreement was observed between the results of training and experimental data. [ABSTRACT FROM AUTHOR]

Climate change, driven by greenhouse gas emissions, is a growing global concern, threatening world-wide environment, health and economy. Energy needs for buildings are a large source of greenhouse gas emissions. As the energy needs of buildings strongly depends on weather patterns, this paper investigates how climate change may impact building heating and cooling loads, cost-optimal efficiency measures, and renewable energy production. Eight locations (Stockholm, Milan, Vienna, Madrid, Paris, Munich, Lisbon, and Rome) highlight differences among European climates. Weather datasets, commonly used in building energy simulations, are evaluated to see how climatic parameters have changed over recent decades. A future climate change scenario (with uncertainties) is analyzed for the year 2060. Weather files are used to drive building energy simulations for a standard baseline and a (Nearly Zero Energy Building) NZEB residential building whose design is improved using a cost-optimization approach. The analysis indicates most currently available weather datasets cannot assure reliable results with building simulations. We find the energy balance in European buildings will significantly change under future conditions: heating will decrease by 38%–57%, while cooling will increase by +99%–380% depending on location. In future NZEBs, efficiency measures to reduce cooling needs and overheating will be favored (e.g. roof insulation, window type, solar shading, envelope finishes), illustrating how improving energy efficiency will be more crucial within climate change scenarios. Compared to the baseline, more efficient NZEBs will enable renewable energy to much better cover building needs. There will also be advantages from reducing winter and summer peak demand, particularly when coupled to short-term electrical storage. When solar resource is limited in winter, more airtight, better-insulated NZEBs improve PV self-consumption. • Climate change impact is studied in baseline and NZEBs in different climates. • Different weather datasets and a climate change scenario are simulated. • Future heating loads decrease by 38%–57%, cooling increase by +99%–380%. • Energy efficiency measures to reduce cooling and overheating are favored. • NZEBs enable renewable energy to much better cover building needs. [ABSTRACT FROM AUTHOR]

Indirect evaporative cooling (IEC) and heat recovery are key technologies for decarbonising heating, ventilation, and air-conditioning (HVAC) while their performance is affected by the ambient environment. This paper presents a numerical investigation of a recently developed hybrid ventilator integrating the IEC and heat recovery. A TRNSYS-Matlab model was used to evaluate the ventilator's adaptability (operational and energy performance) in a public building at five typical cities in China. According to the validated model analysis, the ventilator shows a superior adaptability to the temperate region, with the lowest annual energy use at 96.70 kWh/m2 and the minimum energy input of the auxiliary source at 39.29% of the total energy use. The hybrid ventilator's adaptability is followed by the hot summer cold winter region and cold region where the ventilator covers 85% of the total heating/cooling loads in transition seasons. The severe cold region and hot summer warm winter region requires a significant auxiliary energy use for the stable indoor air parameters, which consumes 70.64% and 60.1% of the annual energy use, respectively. Future works are to improve the performance of the IEC and heat pump unit in the two climatic regions for better adaptability of the ventilator. • Numerically investigate the climatic adaptability of a hybrid ventilator in China. • The ventilator can process fresh air efficiently in the varying climatic regions. • The ventilator's monthly average EER can reach 5.6 in cold regions. • The adaptability should not be evaluated only by EER alone. • Suggestions are given to achieve better ventilator performance. [ABSTRACT FROM AUTHOR]

In China, the annual energy consumption for cooling represents a relatively large and rising trend. District cooling systems (DCSs) have attracted increasing attention owing to their energy-saving operation and high-efficiency. However, a reasonable operation strategy for DCSs with multi-cold sources is still a problem. The main objective of this paper is to present a computer model to investigate the performance of large-scale DCSs in China based on survey data. To evaluate the performance of the system, we established a baseline model and a DCS model with ice thermal storage (ITS). We also developed a control logic that can respond to real-time load changes in a timely manner for both models. A comparison of the two models under a set control strategy revealed that the carbon dioxide emissions (CDEs) and energy consumption of the DCS and ITS (DCS&ITS) were 9% higher than the baseline model without ITS, but the cumulative cooling capacity also increased by 20% to satisfy users 14% of the cooling load demand. ITS has eased the pressure on the power grid while reducing operating costs. The annual operating cost of DCS&ITS was reduced by 6.78% compared with the baseline model, and the overall system efficiency increased by 9.46%. [Display omitted] • The multi-cold source DCS in subtropical climate was studied. • The baseline model and DCS&ITS model were developed based on TRNSYS. • The ice storage module in TRNSYS and its related mathematical models were elaborated. • The optimal control program of all devices in the DCS was designed. • The performance of two models was evaluated from multiple perspectives. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a model-based optimization strategy for vapor compression refrigeration cycle. Through analyzing each component characteristics and interactions within the cycle, the optimization problem is formulated as minimizing the total operating cost of the energy consuming devices subject to the constraints of mechanical limitations, component interactions, environment conditions and cooling load demands. A MGA (modified genetic algorithm) together with a solution strategy for a group of nonlinear equations is proposed to obtain optimal set point under different operating conditions. Simulation studies are conducted to compare the proposed method with traditional on–off control strategy to evaluate its performance. Experiment results of a real practical system are also presented to demonstrate its feasibility. [Copyright &y& Elsevier]

Abstract: Recent work has proposed that hybridization of residential-scale cogeneration with roof-mounted solar PV (photovoltaic) arrays can increase the PV penetration level in ideal situations by a factor of five. In regions where there is a significant cooling load PV-cogen hybrid systems could be coupled to an absorption chiller to utilize waste heat from the cogen unit. In order to investigate realistic (non-ideal) loads that such a hybrid system would need to service, a new numerical simulation called PVTOM (PV-trigeneration optimization model) was created and coupled to the results of the established CHREM (Canadian Hybrid Residential End-Use Energy and Emissions Model). In this paper, PVTOM is applied to representative houses in select Canadian regions, which experience cooling loads, to assess the fuel utilization efficiency and reduction in greenhouse gas emissions from hybrid PV-cogen and trigen systems in comparison with conventional systems. Results of the optimization runs are provided and the efficacy of PV-cogen and PV-trigen systems is discussed. Both PV-trigen and PV-cogen systems have demonstrated to be more effective at reducing emissions when compared to the current combination of centralized power plants and household heating technologies in some regions. [Copyright &y& Elsevier]

In this paper, the Computational Fluid Dynamics (CFD) technique is used to investigate the influences of varying cooling loads on the ejector pressure recovery performance in an ejector-based multi-evaporator refrigeration system (EMERS) using R134a as the refrigerant. The performance of pressure recovery in the EMERS reflects the performance of the compression energy saving. The developed CFD model is first validated by actual experimental data from the EMERS. Turbulence model constants are carefully selected in order to minimize the model prediction error. Over 200 different cases are studied using the model to find the effects of varying cooling loads on pressure recovery ratio. The results indicate that pressure recovery ratio is very sensitive to the varying primary and secondary flow cooling loads. The maximum pressure recovery ratio can reach 60% as the cooling loads vary. It was found that in order to keep the system stable, the primary and secondary cooling loads should be maintained within ±5% and ±10%, respectively, in which case the pressure recovery ratio will have a maximum ratio of 32.8%. [ABSTRACT FROM AUTHOR]

Abstract: Cooling towers (CTs) that are used for heat rejection in water-cooled (WC) systems consume a large quantity of water, which is generally not available naturally. CTs are selected when the maximal cooling load is desired and under the worst design conditions. Typically, CTs operate under partial-load conditions and/or favorable weather conditions. Moreover, for most of the summer season, the dry bulb temperature (DBT) of the incoming ambient air is significantly greater than the incoming hot water temperature, and the air undergoes sensible cooling. Currently, the control scheme that is commonly used in most CTs maintains a constant exiting water temperature for different cooling loads and a different ambient wet bulb temperature (WBT) by regulating the air circulation through the CT. The air circulation is reduced with the help of a variable frequency drive (VFD), which results in a significant reduction in the fan power of the CT. This paper presents an assessment of CT performance with a VFD application using a computer simulation program and illustrates a proposed scheme for maximal water savings. These theoretical results demonstrated that reducing the air flow by applying a VFD in a CT can achieve at least a 25% reduction in water consumption. [Copyright &y& Elsevier]

Abstract: In common air conditioning methods, both sensible and latent loads are handled by cooling systems. Utilizing desiccant, individual cooling and dehumidification processes are possible. In this paper, desiccant wheel operation has been investigated by experimental study. Experimental conditions include different climates (hot dry and hot humid) at different operating parameters (regeneration temperature and wheel speed). Due to the temperature and humidity measurements of inlet and outlet streams, the desiccant wheel efficiency in each definition was calculated. All experiments show that enthalpy of the outlet process air is notably higher than that of the inlet air. This event leads to a novel efficiency definition which presents the deviation of the outlet process air enthalpy from the inlet air enthalpy. By increase in the dehumidification efficiency, the adiabatic efficiency decreases, whereas it increases by the regeneration efficiency. Hence in some situations the adiabatic efficiency will have an optimum value. According to the adiabatic efficiency concept, it seems to be related to the coefficient of performance of the desiccant cooling systems. [Copyright &y& Elsevier]

Floating nuclear power stations (FNPSs) are being constantly developed due to their potential for broad application. Meteorological parameters are an important basis for heating, ventilation and air conditioning (HVAC) system design and energy estimation. However, existing HVAC meteorological parameters are mostly applicable to inland buildings and are becoming obsolete due to climate change. This paper aims to determine the outdoor design temperature and relative humidity for FNPSs. Raw weather data recorded over a 30-year period from National Oceanic and Atmospheric Administration (NOAA) were used. Considering nuclear safety requirements and energy conservation goals, the HVAC meteorological parameters are classified into safety class parameters for nuclear safety-related systems and non-safety class parameters for conventional systems. In addition, the area of China Sea and its vicinity is subdivided into 18 sea regions to reflect local climate characteristics. For a typical FNPS, the outdoor design temperature proposed is compared with that provided in several standards from the perspective of cooling/heating load. It shows that cooling and heating loads with safety class design temperature are 34.0% and 26.5% less than the maximum design cooling and heating loads, respectively. This study can serve as a reference for the determination of HVAC meteorological parameters of FNPSs. • Weather data sources from NOAA over a 30-year period from 1989 to 2018 are used. • HVAC meteorological parameters for 18 regions of the China Sea area are obtained. • Both safety class and non-safety class parameters for FNPSs are presented. • New parameters decrease 8.8%–34% of cooling load and 10.4%–26.5% of heating load. [ABSTRACT FROM AUTHOR]

Understanding the varying characteristics and aggregate potential of power flexibility from different building types considering regional diversity is critically important to actively engaging building resources in future eco-friendly, low-cost, and sustainable power systems. This paper presents a comprehensive characteristics analysis and potential assessment of the power flexibility from heating, ventilation, and air conditioning loads in commercial buildings in the U.S. using a simulation-based method. Commercial buildings are first grouped by building type and climate region. The U.S. Department of Energy Commercial Prototype Building Models are used to represent an average building in each group and are simulated to characterize power flexibility. Based on building survey data, the number of commercial buildings in each group is estimated and used to calculate aggregate power flexibility. It is found that cooling loads in commercial buildings offer more flexibility for increasing power consumption than for decreasing it. The power consumption of commercial buildings in the U.S. can be increased by 46 GW and decreased by 40 GW on peak summer days. Among all commercial building types, standalone retail buildings provide the most absolute flexibility while medium office buildings have the most flexibility as a percentage of the rated power consumption. • A simulation-based method is proposed to assess flexibility of commercial buildings. • High-fidelity building models in EnergyPlus™ are employed. • Key factors that affect commercial building power flexibility are identified and analyzed. • Aggregate flexibility is estimated by building type and by region for the entire U.S. [ABSTRACT FROM AUTHOR]

Incorporation of phase change materials (PCMs) into buildings has recently attracted widespread attention as they can bridge the mismatch between energy supply and demand through their large energy storage capacity. This paper presents the use of an active PCM storage system in buildings and evaluates its energy performance over the different seasons. To this end, two experimental huts, each equipped with solar and electric heaters in winter or an air conditioning unit in summer, were used to investigate the concept. Also, one of the huts was provided with PCM storage units, and the results obtained were compared with those collected from the reference hut. A CompactRIO, data acquisition system, powered by LabVIEW real-time software, was used to transfer data for analysis, processing, and communicating with the host computer. In this research, the active PCM storage units could store solar energy in cold seasons or free night cooling in warm seasons for later use and hence reduce the heating/cooling load requirements. An accumulative heating energy savings of 40% in May and 10.3% in June/July 2019, were achieved. The use of PCM for space cooling also led to an accumulative energy-saving of 30% in March/April and 10% in January. • An active PCM storage system was designed for heating/cooling load reduction. • Two identical huts were used to test the potential of active PCM system. • Storing solar energy in winter and free night cooling in summer led to peak load shaving. • Up to 40% accumulative energy saving, over 28 days, was achieved in cold seasons. • Up to 30% accumulative energy saving, over 20 days, was achieved in warm seasons. [ABSTRACT FROM AUTHOR]

Neural networks have been widely used in energy saving and optimization of construction industries, but neural networks based on K-means clustering needs to set the clustering number, which has poor objectivity on the energy consumption prediction of buildings. Therefore, this paper presents novel radial basis function (RBF) based on affinity propagation (AP) clustering to evaluate the energy performance and save the energy of buildings. The number of hidden layer nodes of the RBF are obtained by the AP. Then main factors affecting the energy consumption of buildings are used as inputs and outputs of the RBF to build the energy performance and saving model of buildings. Compared with other neural networks, the effectiveness of the proposed method is demonstrated though University of California Irvine datasets. Finally, the proposed method is applied in energy saving and emission reduction of construction industries. In the first case, doubling the roof area and halving the overall height of buildings are obtained. And the heating and cooling loads of buildings are reduced by 56.35% and 50.06%, respectively. In the second case, the humidity outside is increased by 12.45%. Meanwhile, the temperature outside and the energy consumption of buildings are reduced by 7.04 °C and 31.27 Wh, respectively. • A novel RBF neural network integrated affinity propagation method is proposed. • This proposed model is efficient in energy saving of buildings. • The heating load and the cooling load are reduced by 56.35% and 50.06%. • The energy consumption of building is reduced by 31.27 Wh. [ABSTRACT FROM AUTHOR]

Micro-thermoelectric coolers have great potential in the thermal management of highly integrated electronic devices, especially the local cooling. This paper develops a numerical model to explore the influence of Thomson effect on the cooling performance of micro-thermoelectric coolers, capturing interfacial and size effects. The presented model is validated with a commercial micro-thermoelectric cooler. And, analyses are carried out with respect to micro-thermoelectric coolers of different sizes, under different temperatures and cooling loads. The results indicate that a positive Thomson coefficient can improve the cooling capacity, and higher current and thickness correspond to greater impact of Thomson effect. In addition, the decrease of minimum cooling temperature caused by Thomson effect is even more obvious under higher cooling load. The results also show that the influence of Thomson effect on the maximum cooling temperature difference gradually becomes weaker as the cross area to thickness ratio increases. For heat fluxes of 100 W/cm2 and 200 W/cm2, the minimum cooling temperature can be respectively reduced by 2.1 K and 4.1 K, considering Thomson effect. In addition, for the thickness of 10 μm, the increment of maximum cooling temperature difference gradually declines from 2.2 K to 1.1K as the cross area to thickness ratio increases. • A valid model to assess the influence of Thomson effect on micro- TEC s is developed. • The presented model is validated with a commercial micro- TEC. • Analyses are presented under different sizes, temperatures and cooling loads. • Temperature drop caused by Thomson effect is greater under higher cooling load. • Impact of Thomson effect gets weaker as cross area to thickness ratio increases. [ABSTRACT FROM AUTHOR]