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| 1 | Evaluating the energy impact potential of energy efficiency measures for retrofit applications:A case study with U.S.medium office buildings显示文摘Quantifying the energy savings of various energy efficiency measures(EEMs)for an energy retrofit project often necessitates an energy audit and detailed whole building energy modeling to evaluate the EEMs;however,this is often cost-prohibitive for small and medium buildings.In order to provide a defined guideline for projects with assumed common baseline characteristics,this paper applies a sensitivity analysis method to evaluate the impact of individual EEMs and groups these into packages to produce deep energy savings for a sample prototype medium office building across 15 climate zones in the United States.We start with one baseline model for each climate zone and nine candidate EEMs with a range of efficiency levels for each EEM.Three energy performance indicators(EPIs)are defined,which are annual electricity use intensity,annual natural gas use intensity,and annual energy cost.Then,a Standard Regression Coefficient(SRC)sensitivity analysis method is applied to determine the sensitivity of each EEM with respect to the three EPIs,and the relative sensitivity of all EEMs are calculated to evaluate their energy impacts.For the selected range of efficiency levels,the results indicate that the EEMs with higher energy impacts(i.e.,higher sensitivity)in most climate zones are high-performance windows,reduced interior lighting power,and reduced interior plug and process loads.However,the sensitivity of the EEMs also vary by climate zone and EPI;for example,improved opaque envelope insulation and efficiency of cooling and heating systems are found to have a high energy impact in cold and hot climates. | Yunyang Ye Kathryn Hinkelman Yingli Lou Wangda Zuo Gang Wang Jian Zhang | 2021 | Building Simulation2021，14，5： | 2 |
| 2 | Daily power demand prediction for buildings at a large scale using a hybrid of physics-based model and generative adversarial network显示文摘Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE. | Chenlu Tian Yunyang Ye Yingli Lou Wangda Zuo Guiqing Zhang Chengdong Li | 2022 | Building Simulation2022，15，9： | 2 |
| 3 | Fast and Informative Flow Simulations in a Building by Using Fast Fluid Dynamics Model on Graphics Processing Unit显示文摘 | Zuo Wangda Chen Qingyan | 2010 | Building and Environment2010，45，3： | 1 |
| 4 | Quantifying spatiotemporal variability in occupant exposure to an indoor airborne contaminant with an uncertain source location显示文摘Well-mixed zone models are often employed to compute indoor air quality and occupant exposures.While effective,a potential downside to assuming instantaneous,perfect mixing is underpredicting exposures to high intermittent concentrations within a room.When such cases are of concern,more spatially resolved models,like computational-fluid dynamics methods,are used for some or all of the zones.But,these models have higher computational costs and require more input information.A preferred compromise would be to continue with a multi-zone modeling approach for all rooms,but with a better assessment of the spatial variability within a room.To do so,we present a quantitative method for estimating a room’s spatiotemporal variability,based on influential room parameters.Our proposed method disaggregates variability into the variability in a room’s average concentration,and the spatial variability within the room relative to that average.This enables a detailed assessment of how variability in particular room parameters impacts the uncertain occupant exposures.To demonstrate the utility of this method,we simulate contaminant dispersion for a variety of possible source locations.We compute breathing-zone exposure during the releasing(source is active)and decaying(source is removed)periods.Using CFD methods,we found after a 30 minutes release the average standard deviation in the spatial distribution of exposure was approximately 28%of the source average exposure,whereas variability in the different average exposures was lower,only 10%of the total average.We also find that although uncertainty in the source location leads to variability in the average magnitude of transient exposure,it does not have a particularly large influence on the spatial distribution during the decaying period,or on the average contaminant removal rate.By systematically characterizing a room’s average concentration,its variability,and the spatial variability within the room important insights can be gained as to how much uncertainty is introduced into occupant exposure predictions by assuming a uniform in-room contaminant concentration.We discuss how the results of these characterizations can improve our understanding of the uncertainty in occupant exposures relative to well-mixed models. | John E.Castellini Jr Cary A.Faulkner Wangda Zuo Michael D.Sohn | 2023 | Building Simulation2023，16，6： | 0 |
| 5 | Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence显示文摘Prediction of indoor airflow distribution often relies on high-fidelity,computationally intensive computational fluid dynamics(CFD)simulations.Artificial intelligence(Al)models trained by CFD data can be used for fast and accurate prediction of indoor airflow,but current methods have limitations,such as only predicting limited outputs rather than the entire flow field.Furthermore,conventional Al models are not always designed to predict different outputs based on a continuous input range,and instead make predictions for one or a few discrete inputs.This work addresses these gaps using a conditional generative adversarial network(CGAN)model approach,which is inspired by current state-of-the-art Al for synthetic image generation.We create a new Boundary Condition CGAN(BC-CGAN)model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter,such as a boundary condition.Additionally,we design a novel feature-driven algorithm to strategically generate training data,with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the Al model.The BC-CGAN model is evaluated for two benchmark airflow cases:an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated box.We also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error criteria.The results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5%relative error and up to about 75,ooo times faster when compared to reference CFD simulations.The proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the Al models while maintaining prediction accuracy,particularly when the flow changes non-linearlywith respectto an input. | Cary A.Faulkner Dominik S.Jankowski John E.Castellini Jr Wangda Zuo Philipp Epple Michael D.Sohn Ali Taleb Zadeh Kasgari Walid Saad | 2023 | Building Simulation2023，16，7： | 0 |
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