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Application and evaluation of a pattern-based building energy model calibration method using public building datasets

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Building Simulation 2022年第8期15卷 1385-1400页

作者： Kaiyu Sun Tianzhen Hong Janghyun Kim Barry Hooper Lawrence Berkeley National Laboratory BerkeleyCAUSA National Renewable Energy Laboratory GoldenCOUSA San Francisco Deportment of the Environment San FranciscoCAUSA

Building performance simulation has been adopted to support decision making in the building life cycle.An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations.Building energy model calibration is a technique that takes various types of measured performance data(e.g.,energy use)and tunes key model parameters to match the simulated results with the actual measurements.This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings.A public building dataset that includes high-level building attributes(e.g.,building type,vintage,total floor area,number of stories,zip code)of 111 buildings in San Francisco,California,USA,was used to generate building models in EnergyPlus.Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’monthly electricity and natural gas consumption.The results showed 57 out of 111 buildings were successfully calibrated against actual buildings,while the remaining buildings showed opportunities for future calibration improvements.Enhancements to the pattern-based model calibration method are identified to expand its use for:(1)central heating,ventilation and air conditioning(HVAC)systems with chillers,(2)space heating and hot water heating with electricity sources,(3)mixed-use building types,and(4)partially occupied buildings.

关键词： model calibration building energy modeling EnergyPlus building performance simulation monthly energy use

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A Novel model calibration Method for Active Magnetic Bearing Based on Deep Reinforcement Learning

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Guidance, Navigation and Control 2023年第3期3卷 110-128页

作者： Bingyun Yang Cong Peng Fei Jiang Sumu Shi College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106P.R.China

Active magnetically suspended control moment gyro is a novel attitude control actuator for satellites.It is mainly composed of rotor,active magnetic bearing(AMB)and motor.As a crucial supporting component of control moment gyro,the performance of AMB is directly related to the stability of the rotor system and pointing precision of the satellites.Therefore,calibrating the parameters of AMB is essential for the realization of super-quiet satellites.This paper proposed a model calibration method,known as the deep reinforcement learningbased model calibration frame(DRLMC).First,the dynamics of magnetic bearing with damage degradation over its life cycle are modeled.Subsequently,the calibration process is formulated as a Markov Decision Process(MDP),and reinforcement learning(RL)is employed to infer the degradation parameters.In addition,experience replay and target network update mechanism are introduced to guarantee stability.Simulation results demonstrate that the proposed method identi¯es force-current factor of AMB during its degradation process e®ectively.Furthermore,additional experiments con¯rm the robustness of the DRLMC approach.

关键词： Control moment gyro active magnetic bearing model calibration reinforcement learning

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A module based modeling framework for estimating lunar dust deposition due to human activities

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Chinese Journal of Aeronautics 2024年第2期37卷 501-510页

作者： Chunjian PAN College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China

Lunar dust is considered to be one of the top challenges for enabling humans to have extended stays on the moon.Human activities such as module landings and launches,walking,rover operation and construction activities will inevitably produce a significant amount of dust.Therefore,it is important to estimate the potential range and intensity of dust deposition caused by these activities to minimize dust accumulation over time and for maintenance planning and execution.A modular model that correlates the dust deposition distribution with initial mean dust particle velocity,its mean ejected angle and the total amount of ejected mass is developed for an elementary mechanical movement.This modular model is further employed to form a modeling framework to estimate dust deposition of a trajectory based activity of similar repeated movements such as the landing process of a lander,walking and rover operation.The model forms a unified modeling framework for different trajectory-based activities and is shown to predict consistent and physically meaningful ranges and intensities of dust deposition provided reliable data to calibrate the model parameters.

关键词： Lunar dust Dust deposition Lunar lander Lunar construction model calibration

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Effects of temporal variability on HBV model calibration

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Water Science and Engineering 2015年第4期8卷 291-300页

作者： Steven Reinaldo Rusli Doddi Yudianto Jin-tao Liu Civil Engineering Department Faculty of Engineering Parahyangan Catholic University State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University

This study aimed to investigate the effects of temporal variability on the optimization of the Hydrologiska ByrS.ns Vattenbalansavedlning （HBV） model, as well as the calibration performance using manual optimization and average parameter values. By applying the HBV model to the Jiangwan Catchment, whose geological features include lots of cracks and gaps, simulations under various schemes were developed： short, medium-length, and long temporal calibrations. The results show that, with long temporal calibration, the objective function values of the Nash- Sutcliffe efficiency coefficient （NSE）, relative error （RE）, root mean square error （RMSE）, and high flow ratio generally deliver a preferable simulation. Although NSE and RMSE are relatively stable with different temporal scales, significant improvements to RE and the high flow ratio are seen with longer temporal calibration. It is also noted that use of average parameter values does not lead to better simulation results compared with manual optimization. With medium-length temporal calibration, manual optimization delivers the best simulation results, with NSE, RE, RMSE, and the high flow ratio being 0.563 6, 0.122 3, 0.978 8, and 0.854 7, respectively; and calibration using average parameter values delivers NSE, RE, RMSE, and the high flow ratio of 0.481 1, 0.467 6, 1.021 0, and 2.784 0, respectively. Similar behavior is found with long temporal calibration, when NSE, RE, RMSE, and the high flow ratio using manual optimization are 0.525 3, -0.069 2, 1.058 0, and 0.980 0, respectively, as compared with 0.490 3, 0.224 8, 1.096 2, and 0.547 9, respectively, using average parameter values. This study shows that selection of longer periods of temooral calibration in hvdrolouical analysis delivers better simulation in general for water balance analysis.

关键词： HBV model model calibration Jiangwan Catchment Temporal variability

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Two-stage nested optimization-based uncertainty propagation method for model calibration

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International Journal of modeling, Simulation, and Scientific Computing 2016年第1期7卷 22-38页

作者： Xiaochao Qian Wei Li Ming Yang Control and Simulation Center Harbin Institute of Technology 150080 HarbinP.R.China

model calibration is the procedure that adjusts the unknown parameters in order to fit the model to experimental data and improve predictive capability.However,it is difficult to implement the procedure because of the aleatory uncertainty.In this paper,a new method of model calibration based on uncertainty propagation is investigated.The calibration process is described as an optimization problem.A two-stage nested uncertainty propagation method is proposed to resolve this problem.Monte Carlo Simulation method is applied for the inner loop to propagate the aleatory uncertainty.Optimization method is applied for the outer loop to propagate the epistemic uncertainty.The optimization objective function is the consistency between the result of the inner loop and the experimental data.Thus,different consistency measurement methods for unary output and multivariate outputs are proposed as the optimization objective function.Finally,the thermal challenge problem is given to validate the reasonableness and effectiveness of the proposed method.

关键词： Simulation uncertainty propagation epistemic uncertainty model calibration

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Using urban building energy modeling to quantify the energy performance of residential buildings under climate change

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建筑模拟(英文) 2023年第9期16卷 1629-1643页

作者： Zhang Deng Kavan Javanroodi Vahid MNik Yixing Chen College of Civil Engineering Hunan UniversityChangsha 410082China Division of Building Physics Department of Building and Environmental TechnologyLund UniversitySE-22363 LundSweden CIRCLE–Centre for Innovation Research Lund UniversityBox 11822100 LundSweden Key Laboratory of Building Safety and Energy Efficiency of Ministry of Education Hunan UniversityChangsha 410082China

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization.Urban building energy modeling(UBEM)is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations,supporting the implementation of carbon emission reduction policies.Currently,most studies focus on the energy performance of archetype buildings under climate change,which is hard to obtain refined results for individual buildings when scaling up to an urban area.Therefore,this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas,by taking two urban neighborhoods comprising 483 buildings in Geneva,Switzerland as case studies.In this regard,GIS datasets and Swiss building norms were collected to develop an archetype library.The building heating energy consumption was calculated by the UBEM tool—AutoBPS,which was then calibrated against annual metered data.A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%.The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).The results showed a decrease of 22%–31%and 21%–29%for heating energy consumption,an increase of 113%–173%and 95%–144%for cooling energy consumption in the two neighborhoods by 2050.The average annual heating intensity dropped from 81 kWh/m^(2) in the current typical climate to 57 kWh/m^(2) in the SSP5-8.5,while the cooling intensity rose from 12 kWh/m^(2) to 32 kWh/m^(2).The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7%and 18.6%,respectively,in the SSP scenarios.The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

关键词： urban building energy modeling climate change model calibration AutoBPS heating and cooling energy consumption

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Parameter estimation for building energy models using GRcGAN

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Building Simulation 2023年第4期16卷 629-639页

作者： Hansol Shin Cheol-Soo Park Department of Architecture and Architectural Engineering College of EngineeringSeoul National UniversitySeoul08826R.O.Korea Institute of Construction and Environmental Engineering Institute of Engineering ResearchCollege of EngineeringSeoul National UniversitySeoul08826R.O.Korea

Parameter estimation methods can be classified into(1)manual(trial-and-error),(2)numerical optimization(optimization,sampling),(3)Bayesian inference(Bayes filter,Bayesian calibration),and(4)machine learning(generative model).Bayesian calibration has been widely used because it can capture stochastic nature of uncertain parameters.However,the results of Bayesian calibration could be biased by(1)the prior distribution assumed by the expert’s subjective judgment;(2)the likelihood function that cannot always describe the true likelihood;and(3)the posterior distribution approximation method,such as the Markov Chain Monte Carlo,which requires significant computation time.To overcome this,a new approach using a generator-regularized continuous conditional generative adversarial network(GRcGAN)is presented in this paper.Five target parameters of the DOE reference building model were selected.GRcGAN was trained to estimate uncertain parameters using simulated monthly electricity and gas use.GRcGAN can successfully estimate five uncertain parameters based on 1,000 training data points.The proposed approach presents a potential for stochastic parameter estimation.

关键词： generative adversarial networks generative model parameter estimation inverse problem model calibration parameter uncertainty

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Turbulence model performance for ventilation components pressure losses

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Building Simulation 2022年第3期15卷 389-399页

作者： Karsten Tawackolian Martin Kriegel Hermann-Rietschel-Institut EnergyComfort&Health in BuildingsTU BerlinGermany

This study looks to find a suitable turbulence model for calculating pressure losses of ventilation components.In building ventilation,the most relevant Reynolds number range is between 3×10^(4) and 6×10^(5),depending on the duct dimensions and airflow rates.Pressure loss coefficients can increase considerably for some components at Reynolds numbers below 2×10^(5).An initial survey of popular turbulence models was conducted for a selected test case of a bend with such a strong Reynolds number dependence.Most of the turbulence models failed in reproducing this dependence and predicted curve progressions that were too flat and only applicable for higher Reynolds numbers.Viscous effects near walls played an important role in the present simulations.In turbulence modelling,near-wall damping functions are used to account for this influence.A model that implements near-wall modelling is the lag elliptic blending k-εmodel.This model gave reasonable predictions for pressure loss coefficients at lower Reynolds numbers.Another example is the low Reynolds number k-εturbulence model of Wilcox(LRN).The modification uses damping functions and was initially developed for simulating profiles such as aircraft wings.It has not been widely used for internal flows such as air duct flows.Based on selected reference cases,the three closure coefficients of the LRN model were adapted in this work to simulate ventilation components.Improved predictions were obtained with new coefficients(LRNM model).This underlined that low Reynolds number effects are relevant in ventilation ductworks and give first insights for suitable turbulence models for this application.Both the lag elliptic blending model and the modified LRNM model predicted the pressure losses relatively well for the test case where the other tested models failed.

关键词： HVAC ductwork CFD simulation turbulence model model calibration

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Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system

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Building Simulation 2023年第6期16卷 997-1012页

作者： Jie Lu Xiangning Tian Chenxin Feng Chaobo Zhang Yang Zhao Yiwen Zhang Zihao Wang Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhouChina Architectural Design&Research Institute of Zhejiang University Co. Ltd.HangzhouChina Center for Balance Architecture Zhejiang UniversityHangzhouChina Jiaxing Research Institute Zhejiang UniversityHangzhouChina

Digital twin is regarded as the next-generation technology for the effective operation of heating,ventilation and air conditioning(HVAC)systems.It is essential to calibrate the digital twin models to match them closely with real physical systems.Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances.To address this challenge,this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems.This method utilizes clustering algorithms to remove redundant data for achieving data compression.Moreover,a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models.Its basic idea is that once a component model is calibrated by heuristic methods,its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components.By doing so,the calibration process can be guided,so that fewer iterations would be used.The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building.Results show that the proposed clustering compression-based method can reduce computation loads by 97%,compared to the conventional calibration method.And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6%of the time costs.

关键词： heating,ventilation and air conditioning systems model calibration digital twin heuristic methods clustering compression hierarchical calibration

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Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy

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Infectious Disease modelling 2022年第1期7卷 1-15页

作者： Erminia Antonelli Elena Loli Piccolomini Fabiana Zama Department of Mathematics University of BolognaItaly Department of Computer Science and Engineering University of BolognaItaly

This paper presents a new hybrid compartmental model for studying the COVID-19 epidemic evolution in Italy since the beginning of the vaccination campaign started on 2020/12/27 and shows forecasts of the epidemic evolution in Italy in the first six months.The proposed compartmental model subdivides the population into six compartments and extends the SEIRD model proposed in[E.L.Piccolomini and F.Zama,PLOS ONE,15(8):1e17,082020]by adding the vaccinated population and framing the global model as a hybridswitched dynamical system.Aiming to represent the quantities that characterize the epidemic behaviour from an accurate fit to the observed data,we partition the observation time interval into sub-intervals.The model parameters change according to a switching rule depending on the data behaviour and the infection rate continuity condition.In particular,we study the representation of the infection rate both as linear and exponential piecewise continuous functions.We choose the length of sub-intervals balancing the data fit with the model complexity through the Bayesian Information Criterion.We tested the model on italian data and on local data from Emilia-Romagna region.The calibration of the model shows an excellent representation of the epidemic behaviour in both cases.Thirty days forecasts have proven to well reproduce the infection spread,better for regional than for national data.Both models produce accurate predictions of infected,but the exponential-based one perform better in most of the cases.Finally,we discuss different possible forecast scenarios obtained by simulating an increased vaccination rate.

关键词： Compartmental model with vaccine SEIRDV Switched model Hybrid model Forcing function model calibration

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