Stochastic comparison between simplified energy calculation and dynamic simulation

Kim, Young‐Jin; Yoon, Seong-Hwan; Park, Cheolsoo

doi:10.1016/j.enbuild.2013.05.026

Cited by 67 publications

(36 citation statements)

References 13 publications

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“…It is common practice in these studies to focus on the building dynamics and assume ideal heating systems. Although this reveals useful results it leaves the question of the HVAC dynamics unanswered, a point raised by Kim et al [19] and Wauman et al [15] who states that EN13790 should not be used for office buildings with intermittent heating and cooling, something that is common in some countries for residential heating. There remains a need to understand clearly the influence of heating system dynamics on the results of NCMs and consequently the information presented in EPCs.…”

Section: Introductionmentioning

confidence: 99%

The Importance of Heating System Transient Response in Domestic Energy Labelling

et al. 2016

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European National Calculation Methods (NCM), such as the UK Standard Assessment Procedure (SAP), are used to make standardised and simplified assessments of building energy performance. These NCMs contain simplifications to aid ease of use and comparability of resulting Energy Performance Certificates (EPC). By comparing SAP with a modern, dynamic modelling system, this study quantifies internal temperatures and thereby heating energy consumption. Results show that for the considered test house SAP results correspond closely to a dynamic model using an idealistic heating system, with perfect control and instant responsiveness. However, the introduction of a dynamic, physically realistic gas fired boiler and water based heating system to the model results in a consistent increase in internal temperature (0.5˝C) and energy demand (by ca. 1000 kWh/a). Variation of further parameters within the dynamic model, controls and heat source size, are presented and compared to SAP results and assumptions. The inclusion of more realistic dynamics in building energy modelling for NCMs may provide a better basis for effective decision making with respect to a wide range of heating systems.

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Section: Introductionmentioning

confidence: 99%

The Importance of Heating System Transient Response in Domestic Energy Labelling

et al. 2016

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“…The iteration number of MCMC varies by model characteristics such as the number of unknown parameters, the range of prior distribution, etc. For one MCMC process for an individual building, it requires a number of iterations ranging from thousands to tens of thousands: 4,000 iterations in (Kim et al 2013), 5,000 iterations in (Tian and Choudhary 2012), 20,000 iterations in (Tian et al 2014), and 25,000 iterations in (Zhao 2012). When the MCMC process is applied to building stock, it will require additional iterations to represent the distribution of the building stock.…”

Section: Computational Timementioning

confidence: 99%

“…The Bayesian calibration procedure has been used in the individual building analysis for calibration of unknown input (Heo et al 2013(Heo et al , 2015aLi et al 2015aLi et al , 2016Kang and Krarti 2016;Berger et al 2016), retrofit analysis (Heo et al 2013(Heo et al , 2015a, comparison with traditional calibration method (Pavlak et al 2013), use of simplified model (Kim et al 2013;Pavlak et al 2013), influence of uncertainties in the input data (Heo et al 2015b), determination of informative energy data (Tian et al 2016), and meta-model comparison (Kim 2016;Li et al 2016). Fig.…”

Section: Stochastic Building Energy Models For Individual Buildingsmentioning

confidence: 99%

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Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

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“…The results were validated with the dynamic simulation software (TRNSYS). Kim, Yoon and Park (2013) compared the simplified ISO 13970 method and dynamic (EnergyPlus software) simulation approach with regard to uncertainty. The different simulations are tested on a general five-storey office building in the South Korea.…”

Section: Application Of the Iso 13790:2008 Standardmentioning

confidence: 99%

Citybem: An Open Source Implementation and Validation of Monthly Heating and Cooling Energy Needs for 3d Buildings in Cities

Murshed

Picard

Koch

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Cities play an important role in reaching local and global targets on energy efficiency and the reduction of greenhouse gas emissions. In order to determine the potential of energy efficiency in the building sector new planning instruments are required that allow depicting the complete building stock on the one hand and investigate detailed measures on the other hand. To pursue this objective, the ISO 13970:2008 monthly heating and cooling energy model is implemented using an open source based software architecture (CityBEM), in connection with data from 3D city models in the CityGML standard (LOD2). Input parameters such as the building geometry, typology and energy characteristics have been associated with the 3D data. The model has been applied to several urban districts with different numbers of buildings in the city of Karlsruhe. In order to test the accuracy of the implemented model and its robustness, a 3-step validation has been conducted. The comparison of simulation results with results based on a TRNSYS simulation showed acceptable results for the studied application cases. The proposed approach can help urban decision makers to perform a city or district wide analysis of the building energy need which can be further used to prepare future scenarios or renovation plans to support decision making.

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Stochastic comparison between simplified energy calculation and dynamic simulation

Cited by 67 publications

References 13 publications

The Importance of Heating System Transient Response in Domestic Energy Labelling

The Importance of Heating System Transient Response in Domestic Energy Labelling

Review on stochastic modeling methods for building stock energy prediction

Citybem: An Open Source Implementation and Validation of Monthly Heating and Cooling Energy Needs for 3d Buildings in Cities

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