کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6727185 1428915 2018 66 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hierarchical calibration of archetypes for urban building energy modeling
ترجمه فارسی عنوان
کالیبراسیون سلسله مراتبی آرکه تایپ ها برای مدلسازی انرژی ساختمان شهری
کلمات کلیدی
آرکه تایپ ها، استفاده از ساختمان ساختمان، مدل سازی سلسله مراتبی، مدل سازی چندسطحی، کالیبراسیون بیزی پیش بینی، همگنی آرکه تایپ، متر هوشمند،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
The application of building archetypes is a widespread approach used in urban building energy modeling. Working with archetypes has a range of benefits, but it is important that modelers avoid using oversimplified approaches when establishing the archetype as they lead to loss of uncertainty and, consequently, to models with inferior predictive capabilities. In this paper, we propose a multilevel take on the challenge of establishing archetypes. A simultaneous modeling and calibration framework is formulated using Bayesian inference techniques - a technique that allows for the propagation of uncertainty throughout the calibration process. By means of hierarchical modeling, information from training buildings is partially pooled together to form an optimal solution between separate building energy models and a completely pooled model. This enables the inference of uncertain archetype parameters that are less prone to building outliers than what is achieved using ordinary aggregation of individual building estimates. The proposed framework incorporates dynamic building energy modeling of arbitrary temporal resolution where uncertain parameters are fitted for individual building models and the archetype model simultaneously. The application of the framework is demonstrated using case-study data from the Danish residential building stock, containing 3-hourly measurements of energy use for 50 training buildings. The model is tested for the prediction of 100 out-of-sample test buildings' aggregated energy use time series on a holdout validation period. With a prediction error of only NMBE = 2.9% and CVRMSE = 7.8%, the archetype framework promises well for urban modeling applications.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy and Buildings - Volume 175, 15 September 2018, Pages 219-234
نویسندگان
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