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In tropical areas of China, energy saving is an important part of architectural design, and the energy-saving potential of residential buildings has attracted extensive attention. This paper studies the daylighting, ventilation, and building energy consumption in tropical areas to find out the best energy-saving performance parameters. The building model is established by grasshopper, and the parameters of daylighting, ventilation performance and cooling load are simulated. The octopus plug-in in grasshopper is used to calculate the target value iteratively, so as to find the relative optimal value of multiobjective. Finally, the optimized design value is compared with the initial value. The results show that the refrigeration energy consumption is greatly reduced from 188.20 kwh/m2 to 163.02 kwh/m2, the Daylight Autonomy (DLA) is reduced from 60.71% to 58.56%, and the ventilation wind speed is increased from 0.62 to 0.63 m/s. It can be seen from the results that although the daylighting objectives was reduced, the cooling energy consumption is greatly reduced, and the optimized daylighting layout is more balanced and reasonable. Therefore, on the basis of reasonable layout, this optimization study effectively reduces the refrigeration energy consumption and achieves the goal of green energy saving. [ABSTRACT FROM AUTHOR]

The response potential of demand-side resources is becoming increasingly significant in integrated energy system (IES) operations. In addition, to ensure the effective participation of system devices, their actual responsiveness at different timescales should be considered. Based on these considerations, this paper proposes an IES multi-timescale operation optimization strategy that incorporates multiple forms of integrated demand response (IDR) and considers the response characteristics of the equipment. First, the multi-timescale characteristics of IDR are analyzed. Moreover, a multi-timescale operation model of IES that comprises day-ahead, intraday, and real-time stages is further established. In the day-ahead dispatch, a low-carbon economic scheduling model is developed by considering the shifting demand response (DR) and the cost of carbon emissions. In the intraday scheduling, noting that cooling and heat energy transmission possess slow dynamic characteristics, a rolling optimization model for cooling/heating coupled equipment considering load shedding and substituting DR is established. In real-time scheduling, the output of electric/gas coupled equipment is adjusted. Finally, an industrial park-type IES in northern China was selected as an example for a case study. The results show that (1) the IDR multi-timescale response strategy can exploit different types of demand-side flexibility resources. After implementing the shifting DR, the peak-to-valley difference of the electric load curve was reduced by 20%, and the total system cost was reduced by 2.3%. After implementing load shedding, the maximum load differences per unit period of the electric, heat, and cooling load curves decreased by 18.7%, 40.0%, and 68.9%, respectively. (2) By refining the timescale of IES optimization, the proposed model can effectively ensure the energy supply and demand balance of the system under different load scenarios and reduce the system operation cost. After applying the model to simulation in three typical days (transition season, summer, and winter), the penalty costs of lost loads reduce by ¥3650, ¥3807, and ¥3599, respectively, and the total system costs decrease by 17.4%, 16.1%, and 16.2%, respectively. [ABSTRACT FROM AUTHOR]

In recent years, diverse energy has been integrated into the power system, which constitutes a regional integrated energy system (IES). However, the coupling and complementation of multiple energy sources make load forecasting more difficult. For the time‐sequence and non‐linear characteristics of electric load and the complementarity of different energy in IES, this paper proposed an attention‐based convolutional neural network (CNN) combined with long short‐term memory (LSTM) and bidirectional long short‐term memory (BiLSTM) model for short‐term load forecasting in IES. The historical load, temperature, cooling load, and gas consumption of the past 5 days are used as the input features. CNN integrated with attention block is utilized to extract effective features of the load impact factors. Then the load of the next hour is forecasted by the LSTM combined with BiLSTM layers. Finally, the model is verified by the data from an integrated energy park in North China. The results show that the proposed method has better forecasting performance than CNN‐BiLSTM, CNN‐LSTM, BiLSTM, LSTM, backpropagation neural network (BPNN), random forest regression (RFR), and support vector machine regression (SVR). [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) are recognized as a promising passive energy retrofit technology for existing buildings, as they are applied to the internal and external surfaces of the building with less impact on the original structure. However, the effectiveness of PCMs varies from region to region. Taking the Central Plains of China as an example, this paper investigated the effect of phase change temperature and material thickness variation on the energy efficiency of PCM when it was arranged on 14 placements including furniture through EnergyPlus (E+). And further explored the applicability of coupling PCMs with natural ventilation (NV). The results showed that: for the Central Plains of China, PCMs arranged on the internal surface of building roofs were optimal, and the reduction rate of uncomfortable degree hours could reach 38%, 26%, 24% and 40% after integrating PCMs into the roof, floor, external wall and furniture. The uncomfortable degree hour reduction rate per unit mass of PCM was 0.052%/kg for the roof, and the value of furniture was very close to the external wall and floor, which achieved 0.033%/kg. In addition, the coupling of PCM with NV improved the energy efficiency of PCM by more than twice. [ABSTRACT FROM AUTHOR]

In this paper, 16 cities representing four major architectural climate zones of China were selected, and the heating and cooling loads of office building during the period of 1961–2017 were simulated by using TRNSYS software. Daily extreme heating and cooling loads were determined by percentile methods, and the climate change impact on extreme loads was analyzed. The results showed that the days of extreme heating load of office building significantly decreased in almost all the selected cities, whereas there were no significant changes in the days of extreme cooling load. The daily extreme heating load was dominantly affected by the temperature (R2 = 0.43–0.79), but the daily extreme cooling load was related to multiple climate factors, including temperature, relative humidity, and solar radiation. Single temperature can only explain 3–37% of the changes of daily extreme cooling load, and the combination of multiple climate factors can explain 58–85%. Importantly, the effect of temperature on extreme cooling load showed decrease from the cold to hot climate zones, but the impacts of relative humidity and solar radiation gradually increased. These results suggest that the maximum design capacity of heating systems can properly decrease, and the design capacity of cooling systems may not largely increase with the climate warming. In addition, the combined effect of multiple climate factors rather than the single temperature, as well as the different responses of extreme cooling energy consumption to climate change in different climate zones, should be considered when making measures for improving operating efficiency of air-conditioning system. [ABSTRACT FROM AUTHOR]

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

Design days are the fundamental parameters for design cooling load calculations. At present, the coincidence and coupling relationship of meteorological elements are ignored by the design-day selection methods provided in the current standards, which results in conservative and unreasonable designs. This research proposes a method of selecting design days for design cooling load calculation that considers the coincidence and correlation of meteorological elements. First, a joint distribution function is constructed; on this basis, the coincident near-extreme meteorological parameters are selected. Then three meteorological elements from a 30-year meteorological record are integrated into one single normalization parameter. Finally, a design day reflective of the variation trend of near-extreme climate conditions is selected from the meteorological record. This paper uses Tianjin in China as an example to generate the research design day. It was found that the outdoor non-guaranteed rate of the research design day is roughly consistent with the corresponding indoor thermal environment risk level. Compared with the standard Chinese design day, the peak values of the research design day are reduced effectively, and the gap between the indoor thermal environment risk level and the outdoor non-guaranteed rate is narrowed. [ABSTRACT FROM AUTHOR]

A large amount of wind turbine power and photovoltaic power is abandoned in many areas with abundant renewable energy due to thermal-electric coupling, inadequate local consumption capacity, and limited capacity of transmission lines, etc. To solve the above problems, a coordinated dispatching method for integrated energy systems is proposed in this paper. Firstly, the spatiotemporal characteristics of diversified loads in multiple functional areas are introduced, including the inertia and elasticity of heating/cooling loads, the spatiotemporal distribution of electric vehicles, and the optimum transmission distance of diversified loads, etc. Secondly, a coordinated dispatching model of integrated energy systems is proposed, which considers the differences of multiple functional areas and various forms of energy systems. Finally, an actual distribution system in Jianshan District, Haining, Zhejiang Province of China is investigated for demonstrating the effectiveness of the proposed model. The results illustrate that the proposed model could effectively improve the consumption rate of renewable energy and reduce the volatility of renewable energy by considering the coordination of electric vehicles, tie lines, and heating/cooling systems in multiple functional areas. [ABSTRACT FROM AUTHOR]

Highlights • Two-stage planning method of multi-energy system is proposed. • Novel energy pricing strategy is first proposed. • Load aggregator is used to conduct the integrated demand response program. • Model of district heating/cooling network under quantity regulation is built. Abstract This paper proposes a two-stage planning and design method for the multi-energy system (MES), which consists of multiple combined cooling, heating and power (CCHP) systems connected by the district electrical network and district heating/cooling network. In the first stage, the optimal objective is to minimize the annual capital and operating costs of the CCHP systems, as well as to obtain the optimal type and capacity of the equipment using the system parameters and load data. Then, a novel nodal energy pricing strategy is first proposed and calculated using the flow-tracing method. The nodal energy price comprises two parts: (i) nodal producing price and (ii) nodal transmission price. In the second stage, depending on the electrical, heating and cooling prices, a load aggregator (LA) is applied to manage the electrical, heating and cooling loads of the end users using an integrated demand response (IDR) program. The objective of the LA is to minimize the annual consumption expense of the end users and update the loads for the optimization in the first stage. In addition, the IDR program includes electricity-load shifting and flexible electrical, heating/cooling supply. Three cases are presented to demonstrate the effectiveness of the planning and design method and to show the influence of the nodal energy prices on the IDR program as well as the penetration of solar energy based on a reconstructive MES in northern China. [ABSTRACT FROM AUTHOR]

Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry have been developed from either forward or inverse modeling approaches. However, these models usually require extensive computer resources and lengthy computation. This paper discusses the use of the multi-layer perceptron (MLP) model, one of the artificial neural network (ANN) models widely adopted in engineering applications, to estimate the cooling load of a building. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing prestigious commercial building in Hong Kong that houses a mega complex and operates 24h a day. The paper also discusses the practical difficulties encountered in acquiring building-related data. In contrast to other studies that use ANN models to predict building cooling load, this paper includes the building occupancy rate as one of the input parameters used to determine building cooling load. The results demonstrate that the building occupancy rate plays a critical role in building cooling load prediction and significantly improves predictive accuracy. [Copyright &y& Elsevier]

Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of simulation results demonstrate that building occupancy data play a critical role in building cooling load prediction and that their use significantly improves the predictive accuracy of cooling load models. [Copyright &y& Elsevier]

In cold regions, due o the impact of climatic conditions, the heat load in winter and the cooling load in summer are unbalanced. In the long-term operation of the ground-source heat pump (GSHP), the soil heat imbalance phenomenon has still not been successfully solved. Therefore, this study took the GSHP of a public building in the cold area of northern China as the research object. Based on the unit performance data of the system over 8 years and the measured data of the soil temperature field, the long-term operation efficiency of the GSHP in the cold region and the variation law of the soil temperature field were explored. In order to further study the problem of soil heat imbalance, the effect of heat exchange hole groups at different intervals on the underground soil thermal environment after 30 years of operation in the system was simulated, and the optimization scheme of heat exchange hole spacing was proposed. The research results support the improvement and optimization of GSHP design and construction, and have important practical significance for the popularization of GSHPs in cold regions. [ABSTRACT FROM AUTHOR]

This paper investigates the climate change impact on cooling energy consumption of office buildings in four major architectural climate zones in China. The results show that there are apparent differences in the responses of monthly or yearly cooling loads to climatic change in different climates. Dominant factors affecting monthly cooling loads change from dry bulb temperature (DBT) to wet bulb temperature (WBT) with the climate zones changing from severe cold to hot. The yearly cooling load was mainly afftectd by DBT in severe cold climate, whereas the WBT had the dominant effect in the other climates. With the continuous warming climate, the yearly cooling load did not rise significantly for any climate zone except for severe cold climate zone where cooling load had a significant increase ( p  < 0.01). More importantly, the cooling load showed a weak decrease in Guangzhou. These results suggest that different measurements for buildings cooling energy conservation should be made in different climate zones. The humidity effect should be fully considered to improve buildings energy efficiency, especially in the hot climate zones. The continuous increase of temperature in the future does not necessarily increase cooling loads of buildings in the cold, hot summer & cold winter and hot summer & warm winter climate zones, and change trendency of humidity in the future need to be considered to predict cooling energy consumption. [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]

• A PV assisted ground source heat pump and air source heat pump (PVGSHP-ASHP) system was proposed. • Performance for three GSHP systems were simulated and compared in depth. • Economic optimization of the integrated operation of the PVGSHP-ASHP system was carried out. • The proposed system can solve the load imbalance problem in hot summer and cold winter zones while improving the COP of GSHP and reducing the system cost. The ground source heat pump (GSHP) system is widely recognized as an efficient, clean and economical renewable energy technology for buildings, and can be utilized for building cooling, heating and hot water supply. In the hot summer and cold winter zones of China, however, the total thermal load (heating load and hot water load) of GSHP systems is greater than the cooling load, which may cause a significant soil temperature drop and GSHP performance deterioration. To solve the above problems, a photovoltaic (PV) assisted GSHP and air source heat pump system (PVGSHP-ASHP) was proposed. This paper investigated the performance and feasibility of the PVGSHP-ASHP system, using a case campus building in Changsha city, China. The PVGSHP-ASHP system was firstly established by TRNSYS, and then, its operational performance was evaluated and compared with the PV assisted GSHP (PVGSHP) system and the PV assisted GSHP and electric heater (PVGSHP-EH) system. Finally, the economic optimization of the integrated operation of the PVGSHP-ASHP system was carried out with the combined simulation of TRNSYS and GenOpt. The results showed that for the PVGSHP system, the soil temperature reduced from 18.1 °C to 11.8 °C after 15 years of operation, which caused a decrease in the annual coefficient of performance of the GSHP (COP GSHP) and an increase in the annual electricity consumption. Although the PVGSHP-EH system can solve the load imbalance problem, its annual lifecycle cost (ALCC) increased by 39.3 % due to the low efficiency of the EH. The PVGSHP-ASHP system can solve the appeal problem with high annual COP GSHP and COP sys values of 3.49 kW/kW and 4.69 kW/kW, respectively, and only 71,700 kWh of the annual electricity consumption, while its ALCC was the lowest at 99,223 CNY, which was 8.1 % lower than the PVGSHP system. After optimization, the ALCC of the PVGSHP-ASHP system reached the minimized value of 91,158 CNY, with a reduction of 8.1 %. These findings can provide new ways to solve the load mismatch problem of GSHP systems while enhancing the system efficiency in the hot summer and cold winter zones of China. [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: In Hong Kong, conventional air-conditioning (AC) systems cause a high energy consumption and poor indoor thermal comfort. This paper investigated the AC load profiles of three typical local commercial buildings, i.e. office, hotel and retail, and examined the energy consumption of liquid desiccant cooling system (LDCS) for these buildings. Simulation results showed that the cooling load contributed to more than 98% of total annual load, with high latent load proportion from March to September. The high reheating ratio, i.e. from 60% to 90%, is found to be the main reason for the poor thermal comfort. Therefore, the LDCS which could handle sensible and latent loads separately was proposed. However, the electricity-driven LDCS was found not suitable for local commercial buildings. significant energy saving could be achieved by the solar-assisted LDCS system with thermal storage, when the installation area for solar collectors is larger than the minimum required one, i.e. 9.7m2/(kWm−2) for office and 12.7m2/(kWm−2) for hotel buildings. Energy saving of different system types with different installation areas was also studied. The solar-assisted LDCS shows more energy saving in winter but less in summer. Results and conclusions can be useful for researchers and local engineers. [Copyright &y& Elsevier]

Abstract: This paper aims to use detailed modelling techniques and develop a database for assessing in a quick and easy way the energy performance of green roof designs across a range of Chinese climates. The focus is on heating and cooling loads by calculating indoor temperatures using the EnergyPlus simulation tool. The study covers 5328 configurations by varying model parameters such as location, climate, seasonal periods, glazing type, wall insulation levels, roof insulation, soil thickness and condition, and vegetation density characteristics. It was found from the results that green roofs can offer significant energy savings for heating and cooling loads if they are applied on roofs without insulation but only limited energy savings where heavy insulation on the roof is also applied. This database can prove to be a useful tool to the practitioners who want to perform rapid assessments of the potential energy savings that could be gained from a green roof installation in a Chinese location. [Copyright &y& Elsevier]

Abstract: The shading-type building-integrated photovoltaic (BIPV) claddings can act as power generators as well as external shading devices of a building, which reduce the energy consumption of the building. However, there is little information about energy impacts of different tilt angles of the shading-type BIPV claddings. By considering the typical meteorological conditions of Hong Kong, the energy performance of the shading-type BIPV claddings, in terms of the electricity generation and the cooling load reduction, is analyzed in this paper. The optimum tilt angle of PV modules for maximum electricity generation is found to be 20° instead of local latitude. Combining electricity generation and cooling load reduction, it can be concluded that the optimum tilt angles for the first type of the shading-type BIPV claddings vary from 30° to 50°, while the optimum tilt angle for the second type is 0°. [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]

China's rural houses are mostly courtyard-style independent houses. Such houses have certain characteristics, e.g., small mutual influences between houses, strong transformation flexibility, and an easier approach to using the natural environment to develop passive energy-saving characteristics. Therefore, rural houses have large energy-saving potential. In this study, for the first time, the cold alleys between buildings were used as an energy source for passive cooling and ventilation. Traditional houses in Shuhe, China, were used as a case study. The cold alleys in the settlements were used to compensate for the natural conditions in summer, and the cold air in the cold alley was introduced into each room using hot-press ventilation and by employing an accumulation effect from a corresponding patio. The room was ventilated and cooled, and air ducts were used to connect the rooms on both sides of the patio to improve the cooling efficiency. The research variables included the existence or non-existence of wall heat radiation (WHR), and the importance and influence of the WHR on the indoor conditions were verified. The cold air trapped in the new system formed an air partition wall, effectively blocking the direct influence of solar radiation on the room, reducing the heat transfer rate of the residential wall, and consuming part of the heat. In winter, based on using air ducts as supporting members, a glass roof was added to the patio, which improved the heat storage capacity of the patio and turned it into a constant-temperature heater for heating the building interior. Based on calculations, in the new system without WHR, the annual cooling load reduction was 55,417.33 kWh. With WHR, the annual cooling load reduction was 28,537.57 kWh. The annual cooling load of the air insulation wall of the new system was reduced to 1133.7 kWh. In winter, using the glass roof to increase the heat storage capacity of the patio reduced the heating load to 54,537.78 kWh. [ABSTRACT FROM AUTHOR]

This article proposes a novel optimal sizing method for the energy station in the multienergy system (MES) integrated with data center. First, an overall framework of the energy station, data center, and multienergy loads is constructed to describe the interconnection of energy system and information system. Then, the dynamic voltage frequency scaling (DVFS) technique, the thermal inertia, and the deep coupling relationships of the electricity load, waste heat, cooling load, and workload in the data center are respectively modeled. On this basis, the optimal sizing model of the energy station is established, in which the data center can participate in the integrated demand response by the DVFS technique and thermal inertia. The capacity of energy station and the scheduling schemes of energy devices and servers are optimized simultaneously to coordinate the energy flow and information flow. Based on the quasi-steady-state assumption of thermal balance, the model is simplified to a mixed-integer quadratic program problem, and it is solved by Gurobi. Case studies based on a real-world test system in China illustrate that the integration of data center can be utilized to reduce the total cost of energy station planning in MES. [ABSTRACT FROM AUTHOR]

Calculating the aerosol direct radiative effect (ADRE) is significance for estimating the influence of aerosols. However, calculating ADRE through the radiative transfer model at large scales and for long periods is time-consuming. In this paper, a linear relationship between surface albedo and surface shortwave ADRE (ADRE SW) was proposed together with a look-up table to simplify the calculation. The linear relationship is tested and remains reliable as atmospheric properties vary. Validation that compared the results calculated with and without simplifications shows high coefficient of determination (0.97). The time required for calculation with the simplification is only 1/2630th of the unsimplified calculation, which reveals that our method greatly simplified the calculation and maintains high accuracy. Based on the simplification, daily surface ADRE SW under clear-sky conditions over Central and East China from 2001 to 2018 is calculated and analyzed. Central and East China has a regional average aerosol optical depth (AOD) of 0.66 and surface ADRE sw of −34.33 W/m2. The North China Plain (0.83, −41.42 W/m2), The Jianghan Plain (0.79, −40.33 W/m2) and the Yangtze River Delta City Agglomeration (0.90, −46.50 W/m2) feature heavy aerosol loading and a strong cooling effect. The inter-annual AOD and cooling effect decreased by 37.60% and 33.21%, respectively, after the 2011 accord to decrease anthropogenic emissions, proving the success of efforts by the Chinese government to protect environment. A study of daily shortwave aerosol radiative effect efficiency found that sunshine duration is the primary controlling factor. • A method to get daily surface ADRE SW through RT model with SA correction and LUT. • This method greatly decreases the time cost without introducing significant error. • ADRE SW increases since 2011, showing Chinese great progress in reducing emissions. [ABSTRACT FROM AUTHOR]