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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]

When investigating supermarket refrigeration systems, it is essential to consider the refrigeration load as it directly affects the required compressor capacity and energy consumption. Therefore, it is important to have knowledge of the refrigeration load and its main influencing factors. Direct usage of data for the refrigeration load in a whole-year energy simulation can present significant challenges. This is partly due to long lead times for data collection and the challenges posed by missing data. As a result, often there is no complete data set of the refrigeration load for the whole-year. Therefore, a mathematical model can serve as a tool to interpolate and extrapolate the refrigeration load over the course of a year. The model is not intended for transfer to other locations. This paper begins with a overview of correlations found in the literature, followed by the development of an improved model that accounts for the impact of ambient temperature and time on the refrigeration load. Additionally, a novel approach using a neural network is proposed as a second model. Both models are then applied to data from three supermarkets in Europe and the model performance is evaluated. Both models performed acceptably in representing the refrigeration load of all three supermarkets. Especially including the influence of time of day into the first model significantly improves the prediction accuracy. The neural network has the highest prediction performance and can improve the prediction performance by 40% over the improved literature model. [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]

As hot days are getting hotter and more frequent, urban dwelling is expected to increase cooling energy use in current and future climate. The applicability of dynamic building simulation in estimating cooling loads of a city's housing stock can be limited due to lack of fine-grained on-site current and future weather inputs. For predicative modelling of residential cooling energy demand to aid a city's energy supply planning resilient to excessive heat conditions, it requires cooling energy demand projection based on a relational account of (1) the thermal-environmental interaction between housing stocks and urban microclimate conditions, (2) the city dwellers' cooling energy use behaviour, and (3) the city's climate projections. In this paper, we introduce an 'archetype-in-neighbourhood' framework to meet these requirements. Combining empirical urban data modelling and EngeryPlus model calibration, this framework was developed to obtain statistically a maximal cooling energy demand model of a city's housing stock during yearly hottest periods. We applied the framework to multiple datasets selected from Seoul's open urban data sources for the period of 2014–2017 (2014 being the earliest year of data availability, 2017 being the end of the study period), including metered electricity use data of 659 apartment buildings (51,351 households) sampled from 18 city districts. The results show that maximal month cooling energy demand (MMCD, kWh/m2) of Seoul's housing stock can be expressed as a regression function of two determinants: (1) the city's average outdoor temperature during the hottest month period (T ex , °C), and (2) estimated indoor cooling temperature set-point (T in , °C) of the city' housing stock during the same period. Through a k -fold (k = 4) validation, the current regression model (2014–17) was evaluated to have an overall coefficient of determination R2 =0.969. Assuming no housing stock renovation, we applied the model to generate scenarios of maximal month cooling demand in future years according to some of the highest summer temperatures projected for Seoul (RCP8.5 2045, RCP4.5 2047, MM5 2071–2100). We conclude this paper with a brief discussion of the implication for cooling energy supply planning and further work to extend the applicability of this new framework to housing stock adaptation planning and design. [ABSTRACT FROM AUTHOR]

• The harvesting related to the electrical energy production of a BIPV as a shading device and roof top photovoltaic (PV) system was simulated by "SolCel (Ver.2019)" which can consider also an influences of obstacles against a reflected irradiance around a BIPV and evaluate a thermal comfort characteristic of an adjacent room, and a solar irradiance for bifacial PV module, etc. • And this paper describes the ecological criteria to optimize the solar architecture through an ecological convergence of a passive solar architecture and photovoltaic system and newly suggested simulation model for solar architecture, considering results from a remodeling process of a small or medium-sized public building by an ecological application of the photovoltaic system. The solar architecture is defined as a kind of building integrated photovoltaic (BIPV) which the Photovoltaic (PV) modules are deployed to passive solar concepts, to minimize the heating & cooling load, to upgrade the indoor environmental quality and to be adjustable for regional weather and to continuously succeed architectural culture and history. The most ideal form of a BIPV is a multi-functional and ecological convergence through a passive concept application of a photovoltaic module for a building. The solar architecture needs to consider the architectural culture and history of the region through an ecological convergence which is applicable to a passive concept if the environment, energy and comfort problems will be effectively mitigated. The evaluation standard is needed to fulfill these requirements of solar architecture. The renewable energy sources are getting very hot issues, due to the environmental pollution, global warming, and energy shortage, etc. It is reasonable to disseminate the representative energy systems which could be ecologically convergent with the regional architecture. Most renewable energy systems including PV could not be activate or not be environmentally friendly if the systems are planned simply without multi-functional and ecological convergence. This paper describes the ecological criteria to optimize solar architecture through an ecological convergence of a passive solar architecture and photovoltaic system. [ABSTRACT FROM AUTHOR]

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

Dynamic cooling and heating load forecasting of heating, ventilation and air conditioning (HVAC) systems is a basis for optimizing the operation of HVAC systems and can contribute to achieving the effective management for the HVAC systems. This paper proposes a load forecasting method for office buildings based on artificial intelligence and regression analysis, including wavelet transform, support vector machines (SVM), and partial least squares regression (PLS). An office building located in Tianjin, China is taken as the building case study to validate the proposed model. For selecting the input variables, the methods of sensitivity analysis and correlation analysis are used. The results of different prediction horizons, mainly including 1 h ahead, 2 h ahead, 3 h ahead and 24 h ahead forecasting, are finally provided. In order to illustrate the accuracy improvement of the proposed model, the other three models are built to compare with the proposed model. Further, the influence of weather forecast precision on the proposed model is analyzed in this paper. The results indicate that the proposed method can realize dynamic load forecasting with high accuracy for different time horizons. [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]

Due to vertically thermal stratification in indoor spaces, the cooling load calculation is always a challenge in the design of stratified air distribution systems (STRAD), which in turn is crucial to determine the supply air flow rate and the cooling load to be removed by the air conditioning system. In this paper, several cooling load calculation methods, developed in the past 20 years and focused on STRAD systems, were briefly reviewed. It attempts to clarify these methods in terms of their advantages, limitations and suitable ranges of applications. Furthermore, series of numerical simulations with a total number of 56 cases were conducted, and the applications of a novel cooling load calculation method in STRAD systems were investigated. The reliability of the method was validated for STRAD systems with separated locations of return and exhaust grilles, when adopted in three typical spaces with different building heights, namely a small office, a large terraced classroom and a terminal building. Databases of effective cooling load factors ( ECLF i ) for different heat sources distributed in the three spaces were obtained and presented, which can be conveniently used to calculate cooling loads. In addition, the influences of return grill height on the occupied zone cooling load as well as the energy saving potentials of STRAD systems were also clarified. The results presented in this paper are helpful for the design and optimization of STRAD systems. [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]

Cooling load characteristics play a key role to the building, especially in self-sustained building. It is determined mainly by the building envelope thermal performance. This research investigates the effect of the building envelope on cooling load in low latitude and hot-humid area. In this paper, based on cooling load simulations, the effect of reducing the cooling load of building envelope and the life cycle cost was analyzed. In addition, the energy-saving system was established. For the life cycle cost analysis, the cost of each strategy was obtained through a survey in local stores. This paper determining the effect of external walls, windows, roof, shading on the building cooling load, and pointed out the optimum insulation thickness of roof and all orientation exterior wall. And the optimum window structure and exterior shading length was give. [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]

This paper summarises the results of a study to identify and quantify the potential impact of measures and policies to reduce the energy consumption of air conditioning in European countries over a 10-year period. The market penetration of air conditioning in Europe has increased steadily over several decades. On a “business as usual” basis, its energy consumption would increase by over 50% by 2020. Measures to restrict this increase are therefore important, but the savings potential straddles several overlapping areas which are subject to different constraints including those imposed by the replacement rates of systems and products, and the refurbishment rates of existing buildings. This work reported in this paper is based on the use of information from a variety of sources in a highly disaggregated model to assess the “realisable” savings over a 10-year period, taking into account relevant constraints. The paper summarises results at a European level, focussing on those cases that generate the greatest realisable savings. Savings are possible in three areas: improved equipment and system efficiency, reduced cooling loads, and more effective operation. The cases that offer the largest savings provide the basis for recommendations for policy measures, often using existing policy instruments. In addition, areas where further work is needed are identified. More detailed results and information about the study and additional recommendations can be found at http://www.bre.co.uk/searchresults.jsp?category=5&q=energy+management [1] . [ABSTRACT FROM AUTHOR]

• Identifying existing limitations of EnergyPlus with respect to evaluating adaptive facades. • Developing an automatic control strategy based on two indoor sensors through EMS. • Proposing two alternative control strategies to evaluate non-conventional adaptive facades. • Comparing the new methods with reference case scenario based on annual energy consumption. The current development of shading systems which respond to both environmental and human inputs; leads to envisioning an envelope that is multifunctional, responsive and dynamic, named as Adaptive Facades (AFs). The key aspect of any external shading system is to control solar radiation that might cause unwanted thermal and visual stresses for occupants which can lead to higher cooling loads significantly. Recent studies attempted to develop non-conventional AFs such as folding structures to fulfill the incapability of conventional systems (e.g. venetian blinds) for delivering multi-objective user demands due to their different design features. Therefore, in this paper motorized venetian blinds were initially used as conventional AFs in Energy Management System (EMS) of EnergyPlus to develop a reference case study. Furthermore, an automatic control was assigned to venetian blinds based on two indoor sensors to control slat angles on hourly basis, Daylight Glare Index (DGI) and task horizontal illuminance. Then, two new control algorithm were derived as hourly transmittance schedule (hTs) and shade factor through extended calculation methods that were used to modify the properties of a roller shade as to take into account the same adjustment of venetian blinds based on indoor sensors. Finally, a comparison was conducted based on annual energy consumption including heating and cooling loads, in which results showed that both methods have discrepancy lower than 6% and can be alternative solutions to evaluate the performance of non-conventional adaptive systems. However, there are several limitations and applicability of the suggested methodologies that were also discussed. [ABSTRACT FROM AUTHOR]

• Two stages of VOCs and air recuperation for energy-saving and low NMHC. • Column top condensers cooled by high-efficiency and reliable MRJT refrigerator. • Two simple rectification columns with 3–5 stages in tandem for better separation. • Cooling and separation characteristics analysis of petroleum VOCs. • Simple but effective bypass in MRJT for cooling variable loads at 100 and 232 K. Thermodynamic analysis of a volatile organic compounds (VOCs) condensation recovery system combined with mixed-refrigerant J-T (MRJT) refrigeration is illustrated in this paper, which provides an effective and economical choice for petroleum plants. VOCs is compressed, cooled by its recuperation process and separated in two simple rectification columns with 3–5 stages. The cooling powers for condensers at column top are provided by a high efficiency dual MRJT refrigerator at 100 K and 232 K, respectively. The cooling and separation characteristics of petroleum VOCs are also analyzed comprehensively. It is indicated that most of VOCs condensation load locates in high temperature region (≥ 235 K approximately). It might be feasible to cool VOCs from 235 K to 110 K by cold purified air through recuperation. For a typical petroleum VOCs, non-methane hydrocarbon residual of 56 mg Nm−3 is achieved with specific power consumption (SPC) of 0.1289 kWh Nm−3, free of additional purifying units. According to the analysis on the effects of variable VOCs composition and pressure, the cooing loads of top condensers are close related to the performance of VOCs recuperation. C1 and C2 are key components to enhance recuperation at low temperatures and reduce SPC. Exceed C3 could enlarge cold stage condenser load and SPC due degraded cold stage recuperation; while exceed C4-C6 could reduce condenser loads and SPC. Higher VOCs pressure could also decrease SPC. [ABSTRACT FROM AUTHOR]

Across diverse geographies, mini-grids have been employed to provide energy access to off-grid and rural communities. In India, amid the government push to expand grid infrastructure, concerns have been raised about the viability of mini-grid business models that are largely run by private companies. While the role of mini-grids in previously un-electrified areas has been studied extensively, there exists limited empirical evidence regarding their uptake in grid-connected regions. In this paper, we analyze adoption and use of mini-grids among rural communities living in grid-electrified villages. The findings are based on the primary data of 2648 households and 544 non-farm enterprises from 54 villages in the states of Uttar Pradesh and Bihar in India. Our results suggest that despite the presence of central-grid infrastructure, users adopt mini-grid in regions which lack reliable power supply. The mini-grid uptake was found to be relatively higher among non-farm enterprises, which, unlike households, have not received adequate policy attention and support. However, mini-grids were being used only for meeting basic lighting and cooling loads in most cases, primarily due to affordability concerns. Based on our findings, we argue that grid-interactive mini-grids could be used to serve rural enterprises as they value reliable power supply. Yet, the current low levels of electricity demand among non-farm rural enterprises suggest that growth in electricity demand and in turn enterprise productivity would require complementary services and infrastructure. • Insights from a survey of 54 villages having both central grid and solar mini-grid. • Mini-grid use among non-farm enterprises is significant, but low among rural households. • Lack of reliable grid-power supply in surveyed villages is a key driver of mini-grid use. • Rural enterprises currently have low electricity demand. • Mini-grid connections primarily used to meet basic lighting and cooling energy demand. [ABSTRACT FROM AUTHOR]

• User involvement affects Trombe wall performance as low tech passive solar systems. • Participatory action research can improve passive systems applications. • Urban living lab is an effective method for Trombe wall performance assessment. • On site monitoring with post occupancy surveys ensures effective evaluation. Due to extreme climate change events, achieving indoor thermal comfort has become a significant challenge in remote desert areas; particularly with the increasing number of energy shortages in these areas. This study uses participatory action research methodology by means of an occupant centred approach for the design and construction of a Trombe wall system, suitable for passive heating and cooling in hot arid climates. The Trombe wall is used as a low-tech retrofitting passive solution to provide deprived communities in off-grid desert areas with a better indoor climate. The paper presents data from one year of monitoring and post occupancy evaluation for the Trombe wall installed as a retrofit in a residential unit in Sinai, Egypt as a proof of concept. Available affordable local materials were of main concern because of the project's remote location in a mountainous desert area with very limited natural resources. The idea was to involve the local community in the different phases of the project, then train them onsite on how to use the Trombe wall system. Results indicate that the use of the Trombe wall did in fact enhance indoor heating and cooling loads. In addition, the direct involvement of the local inhabitants proved to have a positive impact on the Trombe wall's performance and efficiency. The discussion elaborates on key lessons learned and challenges faced from the urban living lab experience presented in the study. Lastly, recommendations for further implementation of the passive Trombe wall system are presented. [ABSTRACT FROM AUTHOR]

The energy consumption necessary to maintain thermal comfort in buildings remains a significant and partially unjustified part, accounting for about 30–40% of the total energy consumption. Despite the increasingly pressing regulations in the energy sector (EPBD recast), the quality of the internal environment remains one of the critical parameters on which to pay more attention in view of a consistent evaluation of the Nzeb building. The windowed components are often designed with thermal insulation performances higher than the minimum requirements but still they lack in design of the incident solar radiation control, which is a particularly delicate topic for indoor comfort and more generally for a higher energy efficiency in buildings. If we think of the numerous variables of influence (typology, orientation, geographical location, etc.), the possibilities to associate energy saving, lighting control and users' comfort are inevitably entrusted to dynamic systems integrated into the glass. In this paper authors present the results related to the experimental characterization of two identical test rooms with different typologies of windows. One room is furnished with a traditional high thermal performance glass and the other, with an electrochromic glass suitably controlled by remote. The operative temperature measured in both test rooms have been utilized to verify the simulation model developed to assess the energy saving achievable in a Mediterranean climate, taking properly into account the hourly variations of the climate and the actions on the shading regulation systems. [ABSTRACT FROM AUTHOR]

• Thermal performance of energy pile systems was evaluated by performing field tests and numerical simulations. • The effect of thermal interference on the thermal behavior of energy piles was investigated. • A design chart for considering the thermal interference effect depending on the denseness of heat exchange pipes was provided. In general, the design of closed-loop vertical ground heat exchangers (GHEXs) is to determine the required borehole length for providing the heating and cooling load of a target building. However, the same design procedure cannot be applied to the cast-in-place energy pile since the configuration of the heat exchange pipe inside the pile is too varied and the size of the borehole is relatively large. In this paper, the effect of configuration and denseness of heat exchange pipe inside the energy pile was experimentally and numerically evaluated comparing with conventional GHEXs. First, field experiments were performed for six cast-in-place energy piles constructed in a test bed. The results showed that the cast-in-place energy piles could provide higher thermal performance per unit borehole length than the other conventional types of GHEXs by increasing the heat exchange area along with much longer heat exchange pipes. However, the over-tight layout diminished the improvement of thermal performance due to thermal interference between each pipe loop. In addition, the long-term thermal behavior of energy piles and changes in ground temperature were estimated with numerical simulations. With the simulation results, a design chart was provided for evaluating the effect of thermal interference on energy pile performance. [ABSTRACT FROM AUTHOR]

Thermal insulation has become essential, firstly to ensure the thermal comfort of the occupants and secondly to save energy and that's why we find more and more varieties of insulators (organic, inorganic and synthetic) in the Moroccan market, depending on the need when to make the right choice. This paper presents the theoretical part of a thermal and an energetic study of the envelop of a house located in the city of Marrakesh (Morocco), by using four types of insulators, that are mostly used in Moroccan markets, for different thicknesses to determine the most suitable insulator for Marrakech climate and its optimal thickness. The insulators studied are: Expanded Polystyrene, glass wool, rock wool and wood fiber. The numerical simulations were realized using the software of thermal dynamic simulation TRNSYS. The results show that for a thickness of 8 cm of the wood fiber insulator saves about 7% for heating and 14% for cooling loads. For temperatures, in January temperatures are higher by 0.26 °C, and in July are lower by 0.49 °C. [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]