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Integrated energy system plays a crucial role in achieving low carbon emissions while supplying multiple loads. As the energy Internet continues to evolve, integrated energy system, acting as a cell-level prosumer, exhibits intricate coupling characteristics between multiple energy flows and carbon flows. This paper presents a flexible planning method based on a unified energy-carbon coupled operation model. It can realize blank-paper planning under multiple energy-carbon flow constraints, that is, jointly optimize equipment configuration and capacity size from scratch. The proposed method combines clustering algorithms and Latin hypercube sampling to generate the operational scenarios for renewable resources and energy loads, enhancing the representativeness and reliability of planning solutions. The results demonstrate that the proposed method can effectively meet the requirements of electricity, cooling and heat loads under different seasonal scenarios. Furthermore, it elucidates the corresponding carbon emission distribution across distinct energy flows of the planned system. Compared with the conventional fixed equipment connected planning method, the more flexible blank-paper planning method can achieve annual economic cost savings of about 7.5 % and reduce annual carbon emissions by 6.7 %. • A unified multi-energy-carbon flows operating-planning model is constructed. • A blank-paper planning for equipment configuration and capacity size is implemented. • An operating scenarios is selected to improve the reliability of planning solutions. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]