Search

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

Precooling gaseous hydrogen fuel to a cold temperature before refueling a heavy-duty (HD) hydrogen fuel cell electric vehicle (HFCEV) is essential to avoid overheating the vehicle tank, as well as achieving a high state of charge (SOC). Because a large volume of hydrogen is dispensed during each fill, the need for a shorter fill time amplifies the need to precool each load for refueling a HFCEV. Thus, the design and operation of a precooling unit (PCU), as well as the associated capital and operating costs, plays a pivotal role in any plans for heavy-duty hydrogen refueling stations. In this paper, we present a thermodynamic and technoeconomic analysis of a PCU in a gaseous hydrogen refueling station (HRS) for HD HFCEVs. By employing Argonne National Laboratory's hydrogen station cost optimization and performance evaluation (H2SCOPE) model, the refueling of 50 kg of hydrogen on-board type IV tank at ambient temperatures of 15–45 °C and varying fill rates is simulated. The required degree of precooling temperature to obtain either 100% or the maximum possible SOC is obtained from the simulation. Additionally, the simulation results demonstrate that the average flow rate of hydrogen is approximately 40% lower than the maximum flow rate during a typical fill; which motivates further evaluation of the instantaneous hydrogen mass flow rate profile and suggests the scope of improving precooling unit design. Accordingly, a hybrid strategy of precooling hydrogen has been proposed to address the cooling load by sizing the refrigeration unit for the average flow rate of hydrogen, while supplementing the above average peak hydrogen flow cooling load through thermal buffering. The combined technique enables the downsizing of the original PCU capacity by 25–40% and demonstrates a potential cost reduction of the PCU by approximately 30%, which translates to an installed cost reduction of ∼$125,000 per dispenser. • Hydrogen fill rate during refueling has a strong influence on the precooling load. • Bell-shaped profile of the fill rate advises a scope of improving precooling. • Hybrid strategy of on-demand cooling aided with partial thermal buffer is proposed. • The hybrid precooling approach downsizes the refrigerator capacity by 25–40%. • A potential reduction in the capital cost of precooling unit is 30%. [ABSTRACT FROM AUTHOR]

This paper provides a method for predicting the energy efficiency of buildings using artificial intelligence tools. The scopes is twofold: prediction of the levels of the heating load and cooling load of buildings. A feature of this research is the performance of intellectual analysis in conditions of a limited amount of data when solving the stated tasks. An improved method of augmentation and prediction (input-doubling method) is proposed by processing data within each cluster of the studied dataset. The selection of the latter occurs due to the use of the fast and easy-to-implement k-means method. Next, a prediction is made using the input-doubling method within each separate cluster. The simulation of the method was performed on a real-world dataset of 768 observations. The proposed approach was found to have a high prediction accuracy in the absence of overfitting and high generalization properties of the improved method. Comparison with existing methods showed an increase in accuracy by 40-46% (MSE) compared to SVR with rbf kernel, which is the basis for the improved method, and by 5-12% (MSE) compared to the closest existing hierarchical predictor. [ABSTRACT FROM AUTHOR]

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]

A Trombe wall with thermal catalytic blinds (TC-Blind/TW) for indoor heating/cooling, air purification and shading was proposed. In this paper, we has fully explored the advancement and feasibility of the system from thermal catalytic material preparation and performance testing, system construction and system steady state experiment testing, system heat-mass transfer model establishment and verification, and finally to the analysis of system operation strategy. The main conclusions are as follows: Firstly, thermal catalytic material MnO x -CeO 2 behaved excellent heating and purification performance, the formaldehyde degradation followed L-H model. Secondly, the thermal efficiency and formaldehyde single-through conversion of system during the daytime were between 35%-55% and 32%–42%, respectively. Thirdly, the established heat-mass transfer model had the high accuracy, with the maximum RSMD of 0.8%. Finally, the blinds turning angle has a great influence on the system performance, then the optimal annual operation angles in four cities were suggested. Take Beijing as an example, the annual generated clean air was 11355.6 m3/m2, the reduced heating and cooling loads were 341.2 and 318.3 MJ/m2, respectively. [ABSTRACT FROM AUTHOR]

The current paper proposes a double-flash geothermal system with a dual-pressure organic Rankine cycle binary system. To improve the system's performance, the inlet geofluid is preheated via an indirect mode of a solar system, and an Ejector Refrigeration Cycle system is installed to the geofluid's brine line to produce cooling capacity. Comprehensive investigations are performed to select the Dual-pressure Organic Rankine Cycle s working fluid among various pure substances and zeotropic mixtures that refer to choosing the R245ca/R142b. The system presents 12.12 MW net power at the base mode with a 1.87 MW cooling load and 4.88 years payback period. Some main parameters' change effect on the system is studied. The low-pressure steam turbine's exit pressure influences the cooling load and energetic efficiency. The high-pressure turbine's input pressure affects the total exergy destruction and net power higher than the other. The optimum state of the system is obtained through two energy-economic and exergy-economic assessments. The energy-economic assessment provides a higher cooling load of about 5.7 MW and a lower net power of about 10.8 MW with 4.59 years payback period. The exergy-economic assessment represents 12.22 MW net power and a cooling load of about 1.97 MW with 4.77 years payback period. • A novel solar and geothermal dual-source power and cooling system is proposed. • Different pure and zeotropic working fluids are employed to select the best one. • Applying energy, exergy, and economic analyses and multi-objective optimization. • R245ca/R142b is selected as the best working fluid in the DORC subsystem. • 12.12 MW net power and 26.45% exergetic efficiencies are obtained. [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]

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]

Accurate short-term forecasting of building cooling load (CLF) in an integrated energy system (IES) is essential for effective building energy management. However, the existing CLF models for IES often treat each building as an independent entity and neglect the spatiotemporal correlation among buildings. To address this research gap and achieve accurate CLF, this paper proposes a new hybrid deep learning model that considers spatiotemporal coupling. First, the coupled spatial–temporal features among different buildings were analyzed, and the meteorological factors were screened based on the Spearman's rank order correlation coefficient (SROCC). Second, synchrosqueezing wavelet denoising (SWT) was adopted to denoise the historical cooling load (CL) data, remove high-frequency noise, and improve data quality. Third, the TTGAT-GTC model was constructed for the CLF of an IES. A temporal trend-aware graph attention network (TTGAT) captured the spatial correlation of CL between buildings. A gated temporal convolution layer (GTC) was constructed to extract the trend in the dynamic temporal variation in historical load. Residual and skip connections were applied to avoid gradient disappearance and increase the computational efficiency of the model. To validate the effectiveness of the proposed SWT-TTGAT-GTC model, this paper compared the proposed model with four benchmark models using MAPE, RMSE, MAE, and R2. The experimental results showed that the performance of the proposed CL forecasting model is superior and that the proposed model appropriately introduces the spatio-temporal coupling information between buildings. • The spatiotemporal information between related buildings is appropriately considered in CL. • TTGAT captures the spatial correlation of cooling load between buildings. • GTC extracts the trend in dynamic temporal variation between buildings. • Residual and skip connections are used to avoid gradient disappearance. [ABSTRACT FROM AUTHOR]

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

• A two-step framework for grey-box modeling of building thermal dynamics. • Parameter identification under fluctuating data from direct expansion system. • Determined optimal complexity and data frequency in case study. • New evaluation metric that considers accumulated energy value for load prediction. Thermal dynamics model plays a crucial role of advanced control in the intelligent building. Grey box method is widely used in the modeling of building thermal dynamics based on the operation data of air-conditioner. The performance of direct-expansion (DX) system fluctuated severely during its operation, which leads to the difficulties in identification and evaluation of the building thermal model. In this paper, a two-step identification framework for second-order thermal resistance-capacity network of building based on DX system data is proposed. A case study is conducted on an office building equipped with variable refrigerant flow system in Foshan, China. The results show that the proposed framework can identify the parameters rationally. The model achieves high accuracy for both indoor temperature (Mean absolute errors of 0.74°C) and cooling load prediction (Mean absolute errors of 4453.86W) in a short time interval of 10 minutes. Additionally, the best complexity of the grey-box model for case building and the data acquisition interval is determined. Finally, an improved set of evaluation metrics for thermal load evaluation is proposed and discussed. [ABSTRACT FROM AUTHOR]

The demand response (DR) operation of chiller plants has been approached with model predictive control (MPC) strategies, based on day-ahead electricity price, knowledge of upcoming weather and load profiles. Since chiller plants feature significant nonlinearities across wide variations in ambient and load conditions, nonlinear MPC (NMPC) is the typical alternative to piecewise linear MPC design, however, suffers from high computational loads. In this paper, an NMPC based chiller plant DR operation is proposed based on data-driven Koopman bilinear form (KBF) dynamic model structure. A generalized tangential interpolation based Krylov-subspace model reduction procedure is applied to the KBF model to yield parsimonious model in the Koopman lifted subspace. Then, the McCormick relaxation is applied to convexify the bilinear terms for the constrained optimization problem formulation associated with the MPC design of the reduced-order KBF system. The KBF based convexified MPC design minimizes the overall energy cost that combines the time-of-use (TOU) and demand charges, with the cooling load regulated in real time. The effectiveness of the proposed control strategy is shown with a Modelica-based dynamic chiller plant model via a Functional Mockup Interface (FMI) based Modelica-Python co-simulation platform. • Computationally efficient Koopman bilinear MPC design for chiller demand response. • Data-driven Koopman bilinear model obtained for nonlinear dynamic plant modeling. • Tangential interpolation based Krylov-subspace reduction for Koopman bilinear model. • Convexification of bilinear terms and ∞ -norm cost in the constrained optimization. • Simulation validation with Modelica-Python co-simulation platform based on FMI. [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]

• Residential Solar Blocks are reconceptualised in a hot-arid climate. • GAs are used for MOO of microclimatic and energy performance of the blocks. • Pareto optimal solutions had 12 m interspaces and orientated NE-SW and NW-SE. • Optimal solutions could reduce cooling loads by 31.7% and reduce UTCI by 1.6 °C. • Optimal solutions could increase the percentage of exposed surfaces in winter by 4%. Courtyard blocks are well known for their tempering potential of the thermal conditions in various climates. In this paper, the building heights, orientation and interspaces of courtyard blocks are optimised for the hot-arid climate of Cairo, Egypt. Simulations are conducted using an evolutionary search algorithm within the Grasshopper parametric design environment for Rhino 3D. The Ladybug-tools, plugins of Grasshopper were used to calculate the objective functions, the summer weekly cooling loads and the weekly average Universal Thermal Climate Index (UTCI). For three courtyard block types elongated 1, 2 and 3-fold the width of the inner court, the Pareto optimal sets of the three types all comprised solutions with the minimum interspaces. Orientation 45° was predominant in the square type, whilst orientation 135° was predominant in the elongated types (W/L = 2, 3). Using the weighted sum ranking, with the exception that orientation 45° was preferred in the most elongated type, the optimal solutions reduced cooling loads by up to 31.7% and reduced UTCI by up to 1.6 °C, compared to a reference case. Solar radiation analysis showed that the optimised cases have great potential to increase the percentage of exposed surfaces by 4% to reduce the heating loads in winter. [ABSTRACT FROM AUTHOR]

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

• For modeling non-equilibrium condensation, the wet steam multi-phase model is used. • Water is applied in ejector refrigeration cycle as economical and non-toxic fluid. • Effects of droplets injection at secondary flow causes reducing COP and ER. • In special case, the steam accompanies by droplets at the outlet of ejector. • Designers and operators should consider effects of droplets in ejector performance. Steam ejector plays a key role in the refrigeration system. The water has been used as a working fluid because water is more economical and non-toxic. The operating fluid often enters the evaporator as a water liquid and droplets evaporate by absorbing heat. Cooling load changing causes the vapor in the outlet of the evaporator to be accompanied by droplets. Therefore, there are droplets at the inlet of secondary nozzle and flow should be simulated as a wet steam flow with injection droplets at the secondary flow. In this paper, the effects of droplets injection at secondary flow have been investigated on the performance of the refrigeration cycle. For this purpose, the Eulerian-Eulerian method has been used and the results have been validated with experimental data. Six cases are considered with different wetness and number of droplets in the secondary flow. The results of case 6 show COP, T ¯ outlet , and ER decrease about 22.93%, 15.66%, and 22.93%, respectively. In addition, the steam accompanies by droplets at the outlet of ejector. Therefore, it is essential for designers and operators to model and consider the effects of the droplets on the wall erosion to optimize the wet steam ejector and refrigeration cycle performance. [ABSTRACT FROM AUTHOR]

In this paper, numerical simulations of Diffusion Absorption Refrigeration – DAR cycle were conducted in order to study the performance of two configurations in steady state mode and use ammonia, water, hydrogen as working fluids. The two configurations were numerically simulated using models developed with the commercial flow sheet Aspen HYSYS V10 software. The first configuration where the main simple DAR cycle components were simulated and validated the experimental results. The second configuration where the modified DAR cycle was simulated which has an auxiliary heat exchanger added to the main simple DAR cycle as a modification. It was found that for the same input assumptions and heat applied, the modified DAR cycle shows higher cooling load. Therefore, higher COP than the simple DAR cycle. The optimal operation of the modified cycle is 36 W applied to the generator to achieve a COP of 0.235 instead of 0.209 for simple DAR cycle. The Simulation results show that by using the modified cycle, the COP improves by 12.4%. [ABSTRACT FROM AUTHOR]

In the long-term operation of a geothermal heat pump system, when the cooling load is high, the temperature rise in the ground due to heat accumulation occurs. Changes in ground temperature hinder recovery of the temperature on the heat source side of the heat pump, and the performance of the geothermal system deteriorates because the heat exchange efficiency of the circulating water decreases. This problem can be solved by operating a hybrid geothermal system composed of an additional heat source system to the geothermal system. Therefore, in this paper, a method for operating a hybrid geothermal heat pump system was proposed to solve the degradation of system performance due to the temperature rise in the ground due to heat accumulation. The operating conditions for starting the operation of the hybrid geothermal heat pump system were derived, and the coefficient of performance prediction model was proposed for flow rate control considering the energy of the pump and the heat pump system. In addition, the proposed control method was evaluated for energy saving and performance improvement through simulation, and was also evaluated in terms of economic feasibility. [ABSTRACT FROM AUTHOR]

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

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]

Rural Research Stations (RRS), Refugee Camps Motorhomes and Caravans are in need of a portable Compact Heating, Cooling and Power (CHCP) generating device. Heating and power generation devices with diesel as driving source of energy are available commercially. In this paper, an innovative compact heating, cooling and power generating device which uses renewable hydrogen and suits rural areas, extreme weather conditions and disastrous situations is proposed. The device is driven by renewable hydrogen fuel generated by PV electrolysis as the source of energy. Low vibration low noise Stirling Engine (SE) is used as mechanical energy converter. Generator is used as electrical energy converter and heat pump is used for supplying heating and cooling effects. The proposed device has many advantages such as encouraging the rural tourism, supply rural research stations the heating and/or cooling loads and contribute in saving lives in the extreme weather conditions and disastrous situations. Analysis of the proposed device in terms of workability and performance was performed. Results showed that renewable energy base, electricity independent, silent, low vibration and better performance were achieved (OHR = 2.662) with the current device compared to available devices. • Generating Hydrogen by PV electrolysis. • Hydrogen Combustion for driving Stirling Engine. • Generation Electricity using Stirling Engine. • Driving Heat Pump for Heating or Cooling. [ABSTRACT FROM AUTHOR]

• An elastocaloric ventilation device is dynamically simulated for building cooling. • An optimized, energy-saving logic is tested, including recirculation and partialization. • Across 8 cities in the world, the device could cover up to 70% of the energy load. • Climate-sensitive design guidelines are provided along with technological constraints. • Terminal velocity, ventilation rate and depth are found to be pivotal design parameters. Refrigerants in vapor-compression systems have a global warming potential thousands of times that of carbon dioxide, yet their spread on the market is unrivalled. Elastocaloric systems, based on solid state cooling, feature among the most promising alternatives. In this paper, an elastocaloric device for air ventilation (ECV) composed by parallel and serial connection of multiple shape memory alloy (SMA) films, is investigated via volume-based finite difference simulation in MATLAB and dynamic building simulation in TRNSYS considering eight cities across the globe. The models assume experimentally demonstrated thermal parameters for the elastocaloric phase transformation around room temperature and a single-storey reference building. The ECV operates according to an optimized, energy-saving logic that includes load partialization and recirculation. Parametric analyses suggest that moderate terminal velocities (∼2 m/s) and a climate-specific design aimed at maximizing the use of the ECV device at nominal cooling capacity are key to reach building cooling needs reductions up to 70% in the considered scenarios. Partialization results in enhanced energy flexibility and conservation, whereas recirculation extends the ECV usability to extreme heat conditions. In absolute terms, the ECV works best under hot climates (e.g. Cairo, Dubai, Brisbane), with monthly cooling load reductions about 2/3-fold compared to cold locations (e.g. Milan, Hobart). The performance is extremely sensitive to the ventilation rate. Thermal zones requiring 1 to 2 air changes per hour are best suited. These findings provide initial insight into design criteria, opportunities and limitations on the use of elastocaloric devices for building ventilation to guide future experimental verification. [ABSTRACT FROM AUTHOR]

A load-based testing methodology has recently been developed for laboratory testing of residential cooling equipment with its integrated controls that employs a model to emulate building dynamics and their interaction with equipment controls. This approach is part of a new performance rating standard that utilizes a bin method to estimate a seasonal energy efficiency (CSA EXP-07, 2019; Cheng et al., 2021). However, a "holy grail" for future equipment performance rating is to be able to map equipment performance using load-based testing results and then implement the map as a "model" in building energy simulations to generate seasonal performance ratings that are specific to various building and climate types. With this goal in mind, this paper presents a performance mapping methodology that incorporates a "gray-box" model structure that uses inputs that are consistent with building energy simulation programs (sensible cooling load and equipment inlet conditions) and outputs key performance metrics (total sensible and latent cooling rates, power consumption, and COP). A strategy and a test matrix for training the model with a relatively small number of testing points were established by developing a successive optimization approach to identify the best 12 test conditions to apply for training from a larger set of available data. In order to develop, demonstrate, and validate the modeling and training approach, load-based laboratory testing was set up within psychrometric chambers and testing was performed to generate 39 quasi-steady-state data points over a range of loads and boundary conditions with a test unit operating with its normal integrated controls. The best model trained using the optimal training subset of 12 data points was able to represent the equipment performance across the operating envelope within ± 10 %. [ABSTRACT FROM AUTHOR]

• Thermodynamic study of VCR systems based on CFC, HCFC, HFC and blends, HC, HFO and CO 2 refrigerants. • Determined component exergy destruction, exergetic efficiency, and system efficiency. • TEWI was assessed for each refrigerant to illustrate the environmental impact. • CO 2 is the potential next generation refrigerant in terms of TEWI, cost, and ASHRAE safety. • HFOs are costly but high-performance alternatives to replace currently used HFCs. This paper presents a rigorous exergy analysis of a vapor compression refrigeration system (VCRS) for the AHRI standard cooling operating conditions assessed with a constant cooling load of 50 kW. A comprehensive mathematical model has been developed to evaluate thermodynamic variables of the major components. The analysis is carried out for widely used refrigerants, namely R12, R22, R134a, R152a, R410A; next-generation low GWP refrigerants R32, R1234yf, R1234ze(E); and two natural refrigerants R600a and R744 (CO 2). First law analysis and exergetic efficiency at cycle and component level are investigated for all the refrigerants. The results indicate that R600a is the best, followed by R152a, R1234ze(E), and R1234yf in terms of COP, total exergy destruction, and exergetic efficiency. Moreover, the total equivalent warming impact (TEWI), which is the aggregation of direct impact due to refrigerant leakage and indirect impact due to electricity usage, from each system has also been assessed. The lowest and highest amount of TEWI has been found for R600a and R12 systems, respectively. This study also indicates that CO 2 is a prospective future refrigerant for its lower emission, affordability, availability, non-toxicity, non-flammability, and system compactness when the VCRS is powered by electricity generated from nuclear/renewable sources. [ABSTRACT FROM AUTHOR]

The paper aims to present a supply model to satisfy the required household final energy carriers, freshwater and industrial cooling loads for a deprived area of access that is located in hot and humid climate. To this aim, a multi-generation hybrid solar thermophotovoltaic integrated system with capability of adjusting heat to power ratio dynamically is introduced. Appropriate analytical tools are developed to obtain the optimal capacity of any systems and energy flows subject to the technology and demand constraints, the financial limitation and the constraints defined by the politicians and local government. The demand constraints are identified through the field studies and the comfort conditions. Moreover, the required final energy carriers are quantified by using an engineering software that observe the interaction among the useful energy demands due to the conversion technologies. The large optimization problem caused by the dynamic of the system is reduced through merging the constraints. The constraint coefficient matrix reduced by 91% in the case study with negligible effect on the accuracy. The results show the multi-generation system contribution to meet the local demand is 35%. However, the electricity generated is 83 times higher than the local demand, the difference is sold to the national network. • A supply model is designed based on TPV coupled with a thermal cycle and CPC. •The system is to satisify final energy and fresh water demands. •The system configuration provides opportunity to adjust the heat to power ratio. •A creative approach is presented to lower the computational load. [ABSTRACT FROM AUTHOR]

• Performance of the ejector refrigerator was studied under different ejector profiles. • Significant performance improvement was achieved when using the CRMC–ejector. • The T chill produced by CRMC–ejector was much lower than CONV–ejector. • The CRMC–ejector produced better performance under various working conditions. • The system COP can be increased by 27–63% via the CRMC–ejector. This paper proposes the performance improvement of the ejector refrigerator based water chiller (ejector-based chiller) by means of using different mixing chamber profiles. Two different ejector mixing chamber profiles are constructed which are based on the conventional method (CONV–ejector) and the constant rate of momentum change method (CRMC–ejector). With the different design methods, the ejectors have different flow areas along the ejector's axis; however, they have identical ejector throat diameters of 9 mm. Both ejectors are equipped with two primary nozzles, and hence, they have the same ejector area ratio of 6.3 and 7.9, respectively. The refrigerator is powered by hot water at a temperature of 94–98 °C while the condenser is cooled by cooling water at 29–31 °C. The chilled water temperature (T chill) under various cooling loads and COP is assessed. It is found that the CRMC–ejector produces a much lower chilled water temperature than the CONV–ejector at identical cooling load. The system COP can be increased by 27–63% via the CRMC–ejector. With the change in T hot , both ejectors produce identical T chill ; however, the CRMC–ejector still produces much lower T chill at an identical cooling load. A larger primary nozzle throat yields a higher T chill. [ABSTRACT FROM AUTHOR]

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [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]

Simplified heat pump models are needed for the design of ground heat exchangers used with ground-source heat pump systems. Design engineers commonly have only manufacturer's data with which to construct a heat pump model that can predict heat extraction and heat rejection given building heating and cooling loads and take account of the entering fluid temperatures to the heat pump. Earlier work has used simple polynomials to predict the ratio of heat extraction to heating and heat rejection to cooling. Unfortunately, manufacturers' catalog data do not always provide sufficient support to calculate the polynomial coefficients. Therefore, in the absence of experimental data, simplified and acceptably accurate models are needed to exploit a limited number of operating points. This paper presents a review of available manufacturers' data in North American and European markets. The available data vary from country to country, depending on the standards in use in each country. In particular, data availability is poorer for the European heat pumps compared to North American heat pumps. Then, a range of models is investigated, characterizing the results by the required inputs and the root mean square error and recommendations are provided. Models are validated against catalog and experimental data. • A review of available heat pump manufacturer's data is provided. • Simplified heat pump models are developed for the design of ground heat exchangers. • The proposed models only need manufacturer's catalog data for calibration. • Models' validation against catalog data and experimental measurements is provided. • Proposed simplified models can be used for water-to-water and water-to-air heat pumps. [ABSTRACT FROM AUTHOR]

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

Summer peak electric demand of commercial buildings and districts is typically driven by the cooling load. This paper presents a novel approach to holistically optimize both the air- and water-side of the HVAC system in existing commercial districts. Therefore, this paper is different from the existing literature in its focus on integrated optimization of both air- and water-side of the HVAC system in a district scale as opposed to only airside or waterside. Methodologies proposed in this paper for integrated optimization of air- and water-side are brand-agnostic and their capabilities are demonstrated using a chilled water plant at Colorado School of Mines' campus. Based on the results for the test district, Mines' integrated air- and water-side optimizations of the HVAC system reduces its peak electric demand by 15% and 13%, respectively, for August 19 and 20, 2019. The air- and water-side optimization of the HVAC system resulted in greater savings than that achieved with only the waterside demand optimization method discussed in authors' previous paper focused on existing commercial districts. [ABSTRACT FROM AUTHOR]

Traditional prediction methods of building cooling load fail to consider load changes caused by the time factor, which results in the failure of the best predicting effect and portability. To explore a more accurate cooling load predicting approach, in this paper, a new hybrid model based on decoupled weight decay Adam optimized improved LSTM neural network, denoted as DwdAdam-ILSTM, is proposed. First, the cross-correlation function is used to extract features with a high degree as the input data. Then, the decoupling weight decay adaptive moment estimation (DwdAdam) optimization algorithm is utilized to optimize the parameters of the improved long short term memory (ILSTM) prediction model. Next, the ILSTM is employed to establish a prediction model. Finally, to verify the validity and prediction accuracy of the proposed model, this paper selects the hourly cooling load data of a large commercial building in Xi'an as the research sample. Results of the experiment show that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Variation Coefficient of Root Mean Square Error (CV-RMSE) and Range of Relative Errors (E-R) of DwdAdam-ILSTM predicting the cooling load are 2.2%, 2.74, 5.2%, 0.061, 0.216. Compared with three version variant LSTM neural networks, the MSE and E-R values of this model are decreased by 74%-53%, 42%-49%, and 69%-64%, which has better performance in accuracy and stability. It testifies that the proposed strategy is reliable and effective. Additionally, this model can provide important guidance for daily energy management and multi-energy scheduling of buildings, which is helpful to the application of cooling load prediction. [ABSTRACT FROM AUTHOR]

For cooling equipment management and scheduling optimization, accurate building cooling load forecasting technology is crucial. Currently, the physics-based forecasting models are too complex to achieve, and existing shallow-machine and deep learning algorithms are difficult to capture and retain sequential information from historical building cooling loads, leading to insufficient prediction accuracy. This paper considered the dependency relationship between time-series information in load data and proposed a building load prediction model based on a transformer network to improve the accuracy of building load prediction. This encoder-decoder block-based model can encode and decode all input data, capture sequence information from mapping vectors with user-defined dimensions, and learn important features through the Attention mechanism. In addition, input features were analyzed to verify the importance of each input feature, and to explaine the reasons for the impact of used features on the TRN-based model. Finally, the performance of the proposed model is evaluated using real data from an office building. Compared with other existing methods, the proposed model has the best prediction accuracy (RMSE, MAE, R2 were 0.01, 0.03, and 0.98, respectively), and maintained the best predictive stability over a longer time (uncertainty ranged from −11% to + 11%). The results show that the proposed method can support the development and optimal operation of energy-saving HVAC systems, thereby lowing power consumption. [ABSTRACT FROM AUTHOR]

• Developed an improved attention-based approach for robust cooling load prediction. • Generated hourly cooling loads under diverse occupancy schedules. • Highlighted the impact of temporal characteristics on prediction performances. • Introduced a time representation learning and attention mechanism to improve accuracy. • Combining XGB to enhance prediction accuracy, robustness, and transparency. Space cooling in buildings is responsible for massive energy consumption and carbon emissions. Accurate cooling load prediction can facilitate the implementation of energy-efficiency cooling control strategies in practice. In this paper, an improved attention-based deep learning approach is proposed for robust ultra-short-term cooling load prediction. First, a novel time representation learning is introduced to extract the periodicity and non-periodicity of cooling loads efficiently. Then, long short-term memory with an attention mechanism extracts properly the time steps by identifying the relevant hidden states and learns high-level temporal dependency. The approach additionally incorporates extreme gradient boosting through the error reciprocal method, enhancing the elimination of prediction errors and improving robustness. The study takes Guangzhou as an example and generates cooling loads using diverse occupancy schedules of five building types based on the Chinese National Standard and Typical Meteorological Year data. The approach is evaluated on datasets comprising the cooling loads, meteorological data, and contextual information. Through results analysis, the approach outperforms other models in terms of prediction accuracy and robustness across all building types. Additionally, model interpretation is provided regarding feature importance and attention matrixes, which enhances the understanding and transparency of the final prediction from the proposed approach. [ABSTRACT FROM AUTHOR]

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

• Modeling a multi-energy retailer in the framework of multi-carrier energy systems. • Developing a multi-energy retailer based on gas/cooling/heating/power carriers. • Presenting an IGDT method to cope with uncertainties in the optimization model. • Integrating flexible options, e.g. hybrid energy storage in the optimization model. • Proposing integrated DR to control multiple energy demands simultaneously. Multi-energy systems (MESs) were introduced to enhance the flexibility and efficiency of conventional energy distribution systems. In this new trend, it is possible to supply different energy carriers simultaneously by the multi-energy retailer (MER), under which, without the need to transfer all of them to different locations, the loads are fed centrally, and the customers can purchase all the carriers they need from the desired MER. Motivated by these descriptions, this paper focuses on the optimal scheduling of MER in the integrated energy system, including natural gas, electricity, cooling, and heating, under the information gap decision theory (IGDT) framework. The MER's goal is to maximize profits by considering the uncertainty of the day-ahead power market. The proposed IGDT approach without a probabilistic distribution function analyzes the risk-aversion strategy associated with electricity price uncertainty. In this paper, integrated demand response (IDR) for natural gas, electrical, heating, and cooling loads are developed simultaneously as a flexible demand-side management resource. The proposed model contains multiple technologies owned by MER, including hybrid energy storage technology, combined heat and power, chiller, boiler units, and renewable resources. Numerical results from different cases illustrate the effectiveness of IDR installation in terms of profitability. Besides, the MER can serve multiple demands more efficiently. Under the IGDT approach, the purchasing power cost is reduced up to 24.23%. [ABSTRACT FROM AUTHOR]

• New comprehensive framework for a CCHP-based MG operation. • New CCP approach for guaranteeing confidence level of the whole system. • Proposing a comparative structure for participating in power and heat markets. • Utilizing multi-carrier storage, hybrid chiller, besides new mathematical for CHP. Microgrids (MGs) have a special role in developing several consumers' energy infrastructure and supply in more economical, safer, and sustainable ways. The interaction and mutual relationship between each energy carrier on the reliable performance of other carriers and the high growth of tri-generation technologies in the MG face the optimal performance of such networks with many challenges. Combined cooling, heating, and power (CCHP)-based MGs are a new generation of MGs that simultaneously provide electrical, thermal, and cooling loads. However, the interaction between these carriers is very influential in CCHP-based MG's operation, which is rarely analyzed. Hence, this paper focuses on the operation of CCHP-based MG coupled with hybrid chiller, multi-energy storage, solar and wind power, etc., under the chance-constrained programming (CCP) approach by considering the mutual relationship between carriers. While modeling consumption and wind and solar energies fluctuations, the proposed approach analyzes the violation of the balance constraint for each carrier and subject it to guarantee the corresponding confidence level. Therefore, the degree of dependence of the system on each carrier and the mutual relationship between carriers are analyzed in this work. This paper also presents a new incentive framework for participating in the electricity and heating markets for the system. The proposed model is implemented on the test system, and the results are discussed for different cases for several confidence levels. The results illustrate the importance of the electricity carrier confidence level on the safe performance of the whole system compared to other carriers. Only by increasing the operation cost of the electricity sector by 4.3%, the system's reliable performance is guaranteed with a probability of 98%. [ABSTRACT FROM AUTHOR]

• Detailed configuration of a CO 2 heat pump with parallel compression is described. • A complete CO 2 system layout for simultaneous heating and cooling is presented. • A validated mathematical model with FSD and VSD CO 2 compressors has been developed. • System performance of actual CO 2 system is evaluated over three months of operation. Carbon dioxide (CO 2) is a natural substance and an environmentally benign, safe and economical refrigerant that can be used for cooling and heating systems. Unlike a traditional reversible heat pump system that only works either in heating or cooling, the air-to-air transcritical CO 2 heat pump system reported in this paper is capable of providing simultaneous space heating and cooling through an Air Handling Unit (AHU) at any time. The system with cooling and heating capacities of 210 kW and 110 kW, respectively, was installed in a cinema complex in South Australia, and continuously operated from September to November in 2020. In this paper, the detailed configuration of the CO 2 heat pump system with parallel compression is described. A complete system layout, comprising of cooling, heating and shared circuits, is revealed for the first time. A mathematical model based on highly efficient Bitzer compressors has also been developed in this study. The experimental data recorded in an entire heating period of approximately one hour has been utilised for model validation and analysis. Through the validation, the simulated power consumption for the compressors with Variable Speed Drive (VSD) and Fixed Speed Drive (FSD) has been compared to the experimental data with acceptable agreements. The simulated discharge temperatures agree with the actual temperatures measured within 10%. Moreover, the latest system performance from September to November has been evaluated based on the heating and cooling loads and combined COPs. It was found that the combined COP was relatively stable at around 3 regardless of climate conditions. [ABSTRACT FROM AUTHOR]

• Backup capability of solar and storage differs by region in the U.S. • Small batteries can meet a limited set of critical loads in most counties. • Electric cooling and heating loads are typically hardest loads to backup. • Household building attributes like efficiency can impact results up to 20% • Resiliency value needs to be high to motivate adoption of solar-plus-storage. Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer's resilience value of PVESS must be high to motivate adoption of these systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • A whole system approach to studying a low-temperature HGSHP system. • 3D coupled TH model with realistic boundaries and dynamic heat pump COP. • Design of a HGSHP system to support a 5th generation DHC network. • Different factors on performance of the HGSHP system and ground thermal behaviour. • Ground moisture transfer, cooling process and buried depth are important. Exploitation of shallow ground and its low-grade heat potential is fundamental to designing 5th generation district heating and cooling (DHC) networks. Horizontal ground-source heat pump (HGSHP) systems are a common way to utilize shallow geothermal energy. Realistic estimation and prediction of performance of a HGSHP system and shallow ground thermal behaviour should consider the whole system including building heating and cooling load, heat pump and ground heat exchanger, and the ground. This should be accompanied by realistic atmospheric and ground conditions. In this paper, a three-dimensional coupled thermal–hydraulic model with realistic boundary conditions adopting a whole system approach is presented. Dynamic heat pump coefficient of performance (COP) that depends on seasonal variation of heating/cooling demand and ground conditions are also considered. Model validations are conducted against experimental and analytical results in literatures. The model is applied for evaluating a HGSHP system to support development of a 5th generation DHC network on a potential site in the UK. Several influencing factors, such as ground moisture transfer, building thermal load mode, buried depth of ground loops, and initial ground temperature profile are studied to assess performance efficiency of the HGSHP system and evolution of ground thermal behaviour in response to heat extraction or rejection into the ground. The results show that 5% of the monthly total heat demand of the site could be met by the designed HGSHP system, consisting of 200 U-shaped ground loops buried at the depth of 3 m and pure water as the heat carrier. Overlooking the ground moisture transfer or hyperbolizing the ground saturation would overestimate the load-carrying capacity of the HGSHP system. The HGSHP system is more efficient with a higher heat pump COP under the heating and cooling mode than under the heating-only mode. Predicted performance of the HGSHP system improves with buried depth of the ground loops. The results also show that a 1 ℃ increase in the undisturbed ground temperature could suffice up to 8% of the monthly total heat demand of the site. [ABSTRACT FROM AUTHOR]

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

• The H 2 -O 2 PEMFC adopts a DEAC operation mode to achieve 100 % utilization of reactants. • PEMEC-PEMFC achieves self-sufficiency of reactants with water self-supply. • The number of PEMEC varies according to solar radiation and light time. • The hourly loads of households can be satisfied by adopting the pre-operation mode. Targeting the shortcomings of oxygen waste and reactant supplement of electrolyzer, this paper proposes a novel combined cooling, heating and power (CCHP) system based on proton exchange membrane electrolysis cell (PEMEC) and H 2 /O 2 proton exchange membrane fuel cell (PEMFC) with water self-supply. In this system, H 2 /O 2 PEMFC adopts a dead-ended anode and cathode operation mode to achieve 100 % hydrogen and oxygen utilization and water recovery. For the application scenario of a household in Shanghai, China, the performance of the CCHP system in summer and winter is investigated. When the system gives priority to meeting the electric and cooling load, there will be insufficient heat at the initial stage of operation. The pre-operation model can realize the hourly matching of residential electric, heating and cooling loads. After one day of operation in summer and winter, there are 19.1 kWh and 45.5 kWh electricity surplus, 7.27 × 106 kJ and 1.97 × 106 kJ heat surplus, 1200 mol and 300 mol hydrogen surplus, respectively. Moreover, the estimated payback period for this system is 7.6 years, which will be significantly shortened with the decrease of the cost of photovoltaic panels and PEMEC. [ABSTRACT FROM AUTHOR]

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

• Cooling load characteristics are used to construct multi-cooling scenarios. • A collaborative optimization method is proposed for district cooling systems. • A compatible multi-objective optimization model is established. • The impacts of the chiller configuration on the scheduling potential are discussed. • The impacts of cooling load characteristics on economic benefits are investigated. District cooling systems have been widely used for their high economic and energy performance. However, insufficient consideration of chiller configuration selection and multi-cooling scenarios causes excessive equipment installed capacity and low energy efficiency. This paper proposes a collaborative optimization method and a compatible multi-objective optimization model to address the design and operation issues affecting the economic and energy benefits of district cooling systems. Firstly, the cooling period is divided into different cooling scenarios constructed by cooling load characteristics. Subsequently, the ε-constraint-Sequential Quadratic Programming method is used to solve the multi-objective optimization models under different chiller configurations and various cooling scenarios. Finally, the Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution method is used to get the installation scheme with optimal chiller configuration and the operation strategies under various cooling scenarios. The proposed method is applied to design and optimize a district cooling system with ice thermal storage. The optimal scheme is equipped with double-duty chillers (6 × 1800 RT, 1 × 500 RT) and water-cooled chillers (1 × 1800 RT, 1 × 900 RT), considering chiller configuration selection and multi-cooling scenarios. Compared with the conventional scheme using double-duty chillers (8 × 1800 RT) and water-cooled chillers (2 × 1800 RT), the annual costs and the annual electricity consumption are reduced by 11.03% and 11.78%, respectively. The results verify the effectiveness and advantages of the optimization method. [ABSTRACT FROM AUTHOR]

• A multi-energy optimization model for regional integrated energy system (RIES) considering the uncertainty of source and load sides, where robust optimization (RO) and stochastic optimization (SO) approaches are applied to respectively, is constructed. • The proposed tariff-based demand response (DR) model improves the traditional DR by taking the three kinds of electrical, heating and cooling loads into account in the integrated demand response (IDR) model, realizing peak-cutting and valley-filling. • The energy flows between the energy storage devices and the electric vehicles (EVs) and the coupling devices are also considered in the model, which ensures the comprehensiveness of the model. • The proposed multi-uncertainty model considering energy transaction prices and equipment maintenance cost is compared with different models and scenarios. The results verify the accuracy of the proposed method and the reduction of the economic cost in the RIES. The regional integrated energy system (RIES) is the main direction of the future development of the energy system. However, with the abundant renewable energy sources connecting to the RIES and the diversification of loads, the multiple uncertainties will have an inevitable impact on the operation of the RIES. Aiming at the above problems, this paper proposes a RIES optimization model that considers multiple uncertainties, multi-energy coupling and integrated demand response (IDR). Firstly, a RIES optimization model including renewable energy, upper grid, coupling devices, energy storage equipment, electric vehicles (EVs) and electrical, heating, and cooling load is introduced. Then, by comparing and analysing the mathematical characteristics of the uncertainty of source and load, the robust optimization (RO) is used to model the uncertainty of source and the stochastic optimization (SO) is used to describe the uncertainty of load. Finally, the proposed model is validated with the actual RIES. The results are compared with different cases which show that the proposed model of RIES considering multiple uncertainties and multi-energy coupling can effectively ensure the cooperative operation of the RIES and improve the reliability and economy of the RIES. [ABSTRACT FROM AUTHOR]

The building load forecasting with data-driven technologies guides the operation of the energy system. For new buildings or existing buildings with inadequate monitoring systems, transfer learning can be used to improve load forecasting accuracy. However, without an information integrity judgment, inappropriate migration features and incorrect transfer learning models will reduce the effectiveness of transfer learning. To bridge the gap, a data integrity judgment method is proposed in this paper to determine whether building features are absent. A max-relevance min-redundancy feature selection method with diffusion kernel density estimation (DKDE-mRMR) is established to select the key migrated features. With the transfer component analysis method used as the transfer learning model, the load data of three office buildings are taken as minimum feature set, source domain and target domain respectively for case study. The results prove that the CV-RMSE of load forecasting is over 41.0% by using insufficient information, whereas it can be reduced by 21.6% when information migration is deployed. Even if the target building after feature transfer is further missing the electricity consumption information for cooling load forecast, the CV-RMSE can still be maintained below 21.1% with good robustness. [ABSTRACT FROM AUTHOR]

• The size and shape of a building have considerable influence on both embodied and operational efficiency. • Lower window-to-wall ratios lead to lower heating and cooling across all building types and climates. • Mechanical ventilation with heat recovery significantly increases operational efficiency for most building types and climate zones. • The choice of structural frame type has significant impact on embodied carbon and cost, but negligible influence on heating and cooling loads. • Compactness and low window-to-wall ratios simultaneously benefit embodied and operational efficiency and are decisive to enable meeting the Passivhaus standard. Buildings account for over one-third of global emissions and energy use. Meeting climate pledges will require achieving high operational energy efficiency with low embodied impacts in new construction. Yet, a systematic identification of the relative influence of building design parameters on both operational and embodied efficiencies has rarely been attempted. In this paper we explore for the first time the sensitivity of a wide range of design and operation parameters in terms of embodied carbon, construction cost, as well as heating and cooling loads for multi-storey buildings. We devised a model to estimate the relative importance of a large set of input variables, describing a building's shape, size, layout, structure, ventilation, windows, insulation, air, and use for residential and office multi-storey buildings, across different climates. We found that increasing building compactness, using steel or timber instead of concrete frames, lowering window-to-wall ratio, choosing the most suitable glazing, and employing mechanical ventilation with heat recovery are the most important measures to decrease embodied emissions and operational energy. The most significant trade-offs with construction cost were found for the choice of frame material and in the decision whether to install mechanical ventilation. We estimate that 28–44% of yearly heating and cooling energy and 6 Gt cumulative embodied CO 2 e until 2050 could be saved in multi-storey buildings, without employing new technologies. [ABSTRACT FROM AUTHOR]

• Selected the outdoor design conditions based on simultaneous occurrence probability. • Analyzed the dependence relation between three weather elements using vine copula. • Modeled the multivariate joint distributions of three weather elements. • The indoor thermal risk levels of outdoor design conditions were evaluated. In building design, the commonly used outdoor weather conditions recommended by ASHRAE are generated by the annual cumulative frequency of occurrence method. This may result in air-conditioning equipment capacity being overestimated. This is because the air temperature, humidity and solar radiation that have the greatest impact on indoor peak cooling loads are selected independently. It means that the probability of these three weather parameters occurring at the same time is extremely small. This paper proposes an approach for selecting the design weather data on the basis of the exact joint distribution with specific simultaneous occurrence probabilities of these three weather parameters. For this purpose, a novel statistical method vine copula is applied to construct the theoretical multivariate joint distributions of these three weather parameters for 24-hour data and combined the design parameters of each hour with a specific simultaneous occurrence probability into a design day. The hourly meteorological records over 10 years in Tokyo are used to generate the outdoor design conditions. The results indicate that the vine copula method can create an effective theoretical model and all the design parameters selected by the simultaneous occurrence probability are significantly below that of the same conventional exceeding probability. For simultaneous occurrence probabilities of 0.4 %, 1 % and 2 %, the corresponding indoor thermal risk levels are 0.46 %, 0.52 % and 0.84 %, respectively. Therefore, the proposed method can be used as a reference for designers to determine a more reasonable capacity of air-conditioning systems. [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]

Accurate cooling load prediction has continually attracted interest due to its important role in building energy saving. Much attention to existing methods has been paid to the temporal information of the features that affect the load prediction. However, facing the time-varying interactions between various types of features, existing approaches have limited ability to extract the implicit information of the complex associations among them. This limitation leads to underutilization of features, especially those that indirectly affect the load prediction through such interactions, and further constrain prediction accuracy. To this end, this paper proposed a graph neural network-based cooling load prediction method to integrate the associations and temporal information of the features. Using the weighted mask mechanism, a knowledge embedded dynamic association graph is constructed to handle the time-varying feature associations rationally. By introducing empirical knowledge and adjusting the graph structure automatically according to the real-time data, the proposed method can provide accurate cooling load prediction. Comprehensive experiments are carried out on the onsite data, and the experimental results show the practicability and effectiveness of the proposed method for cooling load prediction. [ABSTRACT FROM AUTHOR]