eQUEST Based Building Energy Modeling Analysis for Energy Efficiency of Buildings

Building energy performance is a function of numerous building parameters.In this study,sensitivity analysis on twenty parameters is performed to determine the top three parameters that have the most significant impact on the energy performance of buildings.Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in the Quick Energy Simulation Tool(eQUEST).The model is calibrated using the Normalized Mean Bias Error(NMBE)and Coefficient of Variation of Root Mean Square Error(CV(RMSE))method.The model satisfies the NMBE and CV(RMSE)criteria set by the American Society of Heating,Refrigeration,and Air-Conditioning(ASHRAE)Guideline 14,Federal Energy Management Program(FEMP),and International Performance Measurement and Verification Protocol(IPMVP)for building energy model calibration.The values of the parameters are varied in two levels,and then the percentage change in output is calculated.Fractional factorial analysis on eight parameters with the highest percentage change in energy performance is performed at two levels in a statistical software JMP.For building A,the top 3 parameters from the percentage change method are:Heating setpoint,cooling setpoint and server room.From fractional factorial design,the top 3 parameters are:heating setpoint(p-value=0.00129),cooling setpoint(p-value=0.00133),and setback control(p-value=0.00317).For building B,the top 3 parameters from both methods are:Server room(pvalue=0.0000),heating setpoint(p-value=0.00014),and cooling setpoint(p-value=0.00035).If the best values for all top three parameters are taken simultaneously,energy efficiency improves by 29%for building A and 35%for building B.

Energy-Efficient and Cost-Effective Approaches through Energy Modeling for Hotel Building

Hotel buildings are currently among the largest energy consumers in the world.Heating,ventilation,and air conditioning are the most energy-intensive building systems,accounting for more than half of total energy consumption.An energy audit is used to predict the weak points of a building’s energy use system.Various factors influence building energy consumption,which can be modified to achieve more energy-efficient strategies.In this study,an existing hotel building in Central Taiwan is evaluated by simulating several scenarios using energy modeling over a year.Energy modeling is conducted by using Autodesk Revit 2025.It was discovered from the results that arranging the lighting schedule based on the ASHRAE Standard 90.1 could save up to 8.22%of energy consumption.And then the results also revealed that changing the glazing of the building into double-layer lowemissivity glass could reduce energy consumption by 14.58%.While the energy consumption of the building could also be decreased to 7.20%by changing the building orientation to the north.Meanwhile,moving the building location to Northern Taiwan could also minimize the energy consumption of the building by 3.23%.The results revealed that the double layer offers better thermal insulation,and low-emissivity glass can lower energy consumption,electricity costs,and CO_(2)emissions by up to 15.27%annually.While adjusting orientation and location can enhance energy performance,this approach is impractical for existing buildings,but this could be considered for designing new buildings.The results showed the relevancy of energy performance to CO_(2)emission production and electricity expenses.

Electromechanical Transient Modeling Analysis of Large-Scale New Energy Grid Connection

The synchronous virtual machine uses inverter power to imitate the performance of the conventional synchronous machine.It also has the same inertia,damping,frequency,voltage regulation,and other external performance as the generator.It is the key technology to realize new energy grid connections’stable and reliable operation.This project studies a dynamic simulation model of an extensive new energy power system based on the virtual synchronous motor.A new energy storage method is proposed.The mathematical energy storage model is established by combining the fixed rotor model of a synchronous virtual machine with the charge-discharge power,state of charge,operation efficiency,dead zone,and inverter constraint.The rapid conversion of energy storage devices absorbs the excess instantaneous kinetic energy caused by interference.The branch transient of the critical cut set in the system can be confined to a limited area.Thus,the virtual synchronizer’s kinetic and potential energy can be efficiently converted into an instantaneous state.The simulation of power system analysis software package(PSASP)verifies the correctness of the theory and algorithm in this paper.This paper provides a theoretical basis for improving the transient stability of new energy-connected power grids.

Modeling,Simulation,and Risk Analysis of Battery Energy Storage Systems in New Energy Grid Integration Scenarios

Energy storage batteries can smooth the volatility of renewable energy sources.The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system(BESS).However,the current modeling of grid-connected BESS is overly simplistic,typically only considering state of charge(SOC)and power constraints.Detailed lithium(Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions.Additionally,there is a lack of real-time batteries risk assessment frameworks.To address these issues,in this study,we establish a thermal-electric-performance(TEP)coupling model based on a multitime scale BESS model,incorporating the electrical and thermal characteristics of Li-ion batteries along with their performance degradation to achieve detailed simulation of grid-connected BESS.Additionally,considering the operating characteristics of energy storage batteries and electrical and thermal abuse factors,we developed a battery pack operational riskmodel,which takes into account SOCand charge-discharge rate(Cr),using amodified failure rate to represent the BESS risk.By integrating detailed simulation of energy storage with predictive failure risk analysis,we obtained a detailed model for BESS risk analysis.This model offers a multi-time scale integrated simulation that spans month-level energy storage simulation times,day-level performance degradation,minutescale failure rate,and second-level BESS characteristics.It offers a critical tool for the study of BESS.Finally,the performance and risk of energy storage batteries under three scenarios—microgrid energy storage,wind power smoothing,and power grid failure response—are simulated,achieving a real-time state-dependent operational risk analysis of the BESS.

Implementation of a particle-in-cell method for the energy solver in 3D spherical geodynamic modeling

The thermal evolution of the Earth’s interior and its dynamic effects are the focus of Earth sciences.However,the commonly adopted grid-based temperature solver is usually prone to numerical oscillations,especially in the presence of sharp thermal gradients,such as when modeling subducting slabs and rising plumes.This phenomenon prohibits the correct representation of thermal evolution and may cause incorrect implications of geodynamic processes.After examining several approaches for removing these numerical oscillations,we show that the Lagrangian method provides an ideal way to solve this problem.In this study,we propose a particle-in-cell method as a strategy for improving the solution to the energy equation and demonstrate its effectiveness in both one-dimensional and three-dimensional thermal problems,as well as in a global spherical simulation with data assimilation.We have implemented this method in the open-source finite-element code CitcomS,which features a spherical coordinate system,distributed memory parallel computing,and data assimilation algorithms.

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

Application of Energy Balance Snowmelt Model on Sunny Slope in Western Tianshan Mountains

[Objective] The study aimed to apply energy balance snowmelt model for estimating the snowmelt runoff generated by seasonal snow in Tianshan Mountains. [Method] Three snow water collecting sites were set on a sunny slope in western Tianshan Mountains to measure the snowmelt rates at hourly interval. The positive sensible heat and negative latent heat fluxes were calculated by the energy balance snowmelt model; the snowmelt rate was also estimated by the model. Finally, the ac- curacy for the model was investigated in detail. [Result] The results indicated that sensible heat fluxes and latent heat fluxes accounted for 13.4% of total energy input and 15.1% of energy output, respectively. A good agreement between observed and estimated SWE was proved by low volume difference and the high Nash-Sutcliff coef- ficients（R2） which were 0.86, 0.92 and 0.91, respectively. [Conclusion] The energy balance snowmelt model has been proved to be a powerful tool for snowmelt estimation.

Energy Balance-Based SWAT Model to Simulate the Mountain Snowmelt and Runoff——Taking the Application in Juntanghu Watershed(China) as an Example

In order to predict long-term flooding under extreme weather conditions in central Asia, an energy balance-based distributed snowmelt runoff model was developed and coupled with the Soil and Water Assessment Tool(SWAT) model. The model was tested at the Juntanghu watershed on the northern slope of the Tian Shan Mountains, Xinjiang,China. We compared the performances of temperature-index method and energy balanced method in SWAT model by taking Juntanghu river basin as an application example(as the simulation experiment was conducted in Juntanghu River, we call the energy balanced method as SWAT-JTH). The results suggest that the SWAT snowmelt model had overall Nash-Sutcliffe efficiency(NSE) coefficients ranging from 0.61 to 0.85 while the physical based approach had NSE coefficients ranging from 0.58 to0.69. Overall, on monthly scale, the SWAT model provides better results than that from the SWAT-JTH model. However, results generated from both methods seem to be fairly close at a daily scale. Thestructure of the temperature-index method is simple and produces reasonable simulation results if the parameters are well within empirical ranges. Although the data requirement for the energy balance method in current observation is difficult to meet and the existence of uncertainty is associated with the experimental approaches of physical processes, the SWAT-JTH model still produced a reasonably high NSE. We conclude that using temperature-index methods to simulate the snowmelt process is sufficient, but the energy balance-based model is still a good choice to simulate extreme weather conditions especially when the required data input for the model is acquired.

Mass Balance Modeling for Electric Arc Furnace and Ladle Furnace System in Steelmaking Facility in Turkey

In the electric arc furnace （EAF） steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then the desired chemical composition of the steel can be obtained in a special furnace such as ladle furnace （LF）. This kind of furnace process is used for the secondary refining of alloy steel. LF furnace offers strong heating fluxes and enables precise temperature control, thereby allowing for the addition of desired amounts of various alloying elements. It also provides outstanding desulfurization at high-temperature treatment by reducing molten steel fluxes and removing deoxidation products. Elemental analysis with mass balance modeling is important to know the precise amount of required alloys for the LF input with respect to scrap composition. In present study, chemical reactions with mass conservation law in EAF and LF were modeled altogether as a whole system and chemical compositions of the final steel alloy output can be obtained precisely according to different scrap compositions, alloying elements ratios, and other input amounts. Besides, it was found that the mass efficiency for iron element in the system is 95.93%. These efficiencies are calculated for all input elements as 8. 45% for C, 30.31% for Si, 46.36% for Mn, 30.64% for P, 41.96% for S, and 69.79% for Cr, etc. These efficiencies provide valuable ideas about the amount of the input materials that are vanished or combusted for 100 kg of each of the input materials in the EAF and LF system.

Modeling assessment of tidal current energy in the Qiongzhou Strait, China

In the present study, an existing three-dimensional finite volume computational ocean model （FVCOM） was refined and configured including an algorithm for computing the power density and mean power density at Qiongzhou Strait of China. The refined model was validated with the measured tidal levels and tidal currents at different gauging stations. The model results are in reasonable agreement with the measured data. Based on the modeling results, we assess the resource of the tidal stream energy in the Qiongzhou Strait and discuss the temporal and the spatial distribution of the tidal current energy there. The conclusion is extracted： the higher power density occurs in the middle area of the strait, and lower at both sides. Characteristics of power density such as the maximum possibility speed, maximum power density during the spring tide period and the neap tide period, have the similar distribution. The southeast part and central area of the strait are of rich tidal current energy, where the maximum possibility speed can reach to 4.6 m/s, and the maximum power density of the spring tide period and the neap tide period can reach 5 996 and 467 W/mz separately in the surface layer The annual mean power density can reach 819 W/m2. Statistical length of accumulative time of the velocity exceeding 0.7 m/s is about 4 717 h at local point during a year. The total theoretical tidal current energy resource is approximately 189.55 MW and the available exploited energy on present technology condition is 249, 20.2 and 263 GW/a separately by using the methods FLUX, FARM and GC in the Qiongzhou Strait.

Modeling Method for Flexible Energy Behaviors in CNC Machining Systems

CNC machining systems are inevitably confronted with frequent changes in energy behaviors because they are widely used to perform various machining tasks. It is a challenge to understand and analyze the flexible energy behaviors in CNC machining systems. A method to model flexible energy behaviors in CNC machining systems based on hierarchical objected-oriented Petri net(HOONet) is proposed. The structure of the HOONet is constructed of a high-level model and detail models. The former is used to model operational states for CNC machining systems, and the latter is used to analyze the component models for operational states. The machining parameters having great impacts on energy behaviors in CNC machining systems are declared with the data dictionary in HOONet models. A case study based on a CNC lathe is presented to demonstrate the proposed modeling method. The results show that it is effective for modeling flexible energy behaviors and providing a fine-grained description to quantitatively analyze the energy consumption of CNC machining systems.

Modelling Wave Transmission and Overtopping Based on Energy Balance Equation

Wave transmission and overtopping around nearshore breakwaters can have significant influence on the transmitted wave parameters,which affects wave conditions and sediment transportation and becomes the focus of design in engineering.The objective of this paper is to present a simplified model to estimate these important wave parameters.This paper describes the incorporation of wave transmission and overtopping module into a wave model for multi-directional random wave transformation based on energy balance equation with the consideration of wave shoaling,refraction,diffraction,reflection and breaking.Wen's frequency spectrum and non-linear dispersion relation are also included in this model.The influence of wave parameters of transmitted waves through a smooth submerged breakwater has been considered in this model with an improved description of the transmitted wave spectrum of van der Meer et al.(2000) by Carevic et al.(2013).This improved wave model has been validated through available laboratory experiments.Then the verified model is applied to investigate the effect of wave transmission and overtopping on wave heights behind low-crested breakwaters in a project for nearshore area.Numerical calculations are carried out with and without consideration of the wave transmission and overtopping,and comparison of them indicates that there is a considerable difference in wave height and thus it is important to include wave transmission and overtopping in modelling nearshore wave field with the presence of low-crested breakwaters.Therefore,this model can provide a general estimate of the desired wave field parameters,which is adequate for engineers at the preliminary design stage of low-crested breakwaters.

Theoretical study of particle and energy balance equations in locally bounded plasmas

In this study,new particle and energy balance equations have been developed to predict the electron temperature and density in locally bounded plasmas.Classical particle and energy balance equations assume that all plasma within a reactor is completely confined only by the reactor walls.However,in industrial plasma reactors for semiconductor manufacturing,the plasma is partially confined by internal reactor structures.We predict the effect of the open boundary area(A′_(L,eff))and ion escape velocity(u_(i))on electron temperature and density by developing new particle and energy balance equations.Theoretically,we found a low ion escape velocity(u_(i)/u_(B)≈0.2)and high open boundary area(A′_(L,eff)/A_(T,eff)≈0.6)to result in an approximately 38%increase in electron density and an 8%decrease in electron temperature compared to values in a fully bounded reactor.Additionally,we suggest that the velocity of ions passing through the open boundary should exceedω_(pi)λ_(De)under the condition E^(2)_(0)?(Φ/λ_(De))^(2).

Modeling of Node Energy Consumption for Wireless Sensor Networks

Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. In this paper, by studying component energy consumption in different node states and within state transitions, the authors present the energy models of the node core components, including processors, RF modules and sensors. Furthermore, this paper reveals the energy correlations between node components, and then establishes the node energy model based on the event-trigger mechanism. Finally, the authors simulate the energy models of node components and then evaluate the energy consumption of network protocols based on this node energy model. The proposed model can be used to analyze the WSNs energy consumption, to evaluate communication protocols, to deploy nodes and then to construct WSN applications.

Dynamics and synchronization of neural models with memristive membranes under energy coupling

Dynamical modeling of neural systems plays an important role in explaining and predicting some features of biophysical mechanisms.The electrophysiological environment inside and outside of the nerve cell is different.Due to the continuous and periodical properties of electromagnetic fields in the cell during its operation,electronic components involving two capacitors and a memristor are effective in mimicking these physical features.In this paper,a neural circuit is reconstructed by two capacitors connected by a memristor with periodical mem-conductance.It is found that the memristive neural circuit can present abundant firing patterns without stimulus.The Hamilton energy function is deduced using the Helmholtz theorem.Further,a neuronal network consisting of memristive neurons is proposed by introducing energy coupling.The controllability and flexibility of parameters give the model the ability to describe the dynamics and synchronization behavior of the system.

Unveiling evapotranspiration patterns and energy balance in a subalpine forest of the Qinghai-Tibet Plateau:observations and analysis from an eddy covariance system

Evapotranspiration is an important parameter used to characterize the water cycle of ecosystems.To under-stand the properties of the evapotranspiration and energy balance of a subalpine forest in the southeastern Qinghai-Tibet Plateau,an open-path eddy covariance system was set up to monitor the forest from November 2020 to October 2021 in a core area of the Three Parallel Rivers in the Qing-hai-Tibet Plateau.The results show that the evapotranspira-tion peaked daily,the maximum occurring between 11:00 and 15:00.Environmental factors had significant effects on evapotranspiration,among them,net radiation the greatest(R^(2)=0.487),and relative humidity the least(R^(2)=0.001).The energy flux varied considerably in different seasons and sensible heat flux accounted for the main part of turbulent energy.The energy balance ratio in the dormant season was less than that in the growing season,and there is an energy imbalance at the site on an annual time scale.

Multi-Model Approach for Assessing the Influence of Calibration Criteria on the Water Balance in Ouémé Basin

Hydrological models are very useful tools for evaluating water resources, and the hydroclimatic hazards associated with the water cycle. However, their calibration and validation require the use of performance criteria which choice is not straightforward. This paper aims to evaluate the influence of the performance criteria on water balance components and water extremes using two global rainfall-runoff models (HBV and GR4J) over the Ouémé watershed at the Bonou and Savè outlets. Three (3) Efficacy criteria (Nash, coefficient of determination, and KGE) were considered for calibration and validation. The results show that the Nash criterion provides a good assessment of the simulation of the different parts of the hydrograph. KGE is better for simulating peak flows and water balance elements than other efficiency criteria. This study could serve as a basis for the choice of performance criteria in hydrological modelling.

Modeling approaches to pressure balance dynamic system in shield tunneling

In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network(ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling(MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.

Analysis of hohlraum energetics of the SG series and the NIF experiments with energy balance model

The basic energy balance model is applied to analyze the hohlraum energetics data from the Shenguang(SG)series laser facilities and the National Ignition Facility(NIF)experiments published in the past few years.The analysis shows that the overall hohlraum energetics data are in agreement with the energy balance model within 20%deviation.The 20%deviation might be caused by the diversity in hohlraum parameters,such as material,laser pulse,gas filling density,etc.In addition,the NIF's ignition target designs and our ignition target designs given by simulations are also in accordance with the energy balance model.This work confirms the value of the energy balance model for ignition target design and experimental data assessment,and demonstrates that the NIF energy is enough to achieve ignition if a 1D spherical radiation drive could be created,meanwhile both the laser plasma instabilities and hydrodynamic instabilities could be suppressed.

Modeling the biomass of energy crops：Descriptions, strengths and prospective

The assessment of the biomass of energy crops has garnered widespread interest since renewable bioenergy may become a substantial proportion of the future energy supply, and modeling has been widely used for the simulation of energy crops yields. A literature survey revealed that 23 models have been developed or adapted for simulating the biomass of energy crops, including Miscanthus, switchgrass, maize, poplar, willow, sugarcane, and Eucalyptus camaldulensis. Three categories（radiation model, water-controlled crop model, and integrated model with biochemical and photosynthesis and respiration approaches） were addressed for the selected models according to different principles or approaches used to simulate biomass production processes. EPIC, ALMANAC, APSIM, ISAM, MISCANMOD, MISCANFOR, SILVA, DAYCENT, APEX and SWAT are radiation models based on a radiation use efficiency approach（RUE） with few empirical and statistical parameters. The Aqua Crop model is a typical water-crop model that emphasizes crop water use, the expression of canopy cover, and the separation of evapotranspiration to soil evaporation and plant transpiration to drive crop growth. CANEGRO, 3PG, Crop Syst and DSSAT are integrated models that use photosynthesis and respiration approaches. SECRETS, LPJm L, Agro-BGC, Agro-IBIS, and WIMOVAC/Bio Cro, DNDC, DRAINMOD-GRASS, and Ag TEM are integrated models that use biochemical approaches. Integrated models are mainly mechanistic models or combined with functional models, which are dynamic with spatial and temporal patterns but with complex parameters and large amounts of input data. Energy crop models combined with process-based models, such as EPIC in SWAT and CANEGRO in DSSAT, provide good examples that consider the biophysical, socioeconomic, and environmental responses and address the sustainability and socioeconomic goals for energy crops. The use of models for energy crop productivity is increasing rapidly and encouraging; however, relevant databases, such as climate, land use/land cover, soil, topography, and management databases, arescarce. Model structure and design assumptions, as well as input parameters and observed data, remain a challenge for model development and validation. Thus, a comprehensive framework, which includes a high-quality field database and an uncertainty evaluation system, needs to be established for modeling the biomass of energy crops.