Demonstration of reduced-order urban scale building energy models

Heidarinejad, Mohammad; Mattise, Nicholas; Dahlhausen, Matthew; Sharma, Krishang; Benne, Kyle; Macumber, Daniel; Brackney, Larry; Srebric, Jelena

doi:10.1016/j.enbuild.2017.08.086

Cited by 43 publications

(15 citation statements)

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“…Similar to this study, the variations in energy use in various other aspects have been considered in recent years, rather than using the average [30][31][32]. While for those considerations a number of models have been established using various methods [33][34], it is also useful to consider simplified mathematical approaches or empirical…”

Section: Discussionmentioning

confidence: 99%

An energy model of high-rise apartment buildings integrating variation in energy consumption between individual units

Jang

Kang

2018

Energy and Buildings

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Building energy modelling methods have been required to be more accurate by taking into account variation in building factors affecting on energy consumption. However, modelling approaches for high-rise apartment buildings have often disregarded variation arising from individual apartment units. This study aimed to develop a building energy model of high-rise apartment buildings by integrating variation derived from individual apartment units. The methods were designed in three steps: identifying unit-specific heating consumption in different locations; creating a building energy model, based on the physical characteristics of apartment units and identifying the influential heating controls on heating energy consumption; and integrating a new set of polynomial model of independent heating controls in units and their interactions between floors. The result indicates that the averaged heating energy consumption of whole-building has a limited interpretation to represent the wide range of heating energy use in apartment units with different locations from 96 to 171 kWh/m 2 /year. The integrated set of polynomial model found that apartment units on lower floors need either higher set-point temperatures or longer heating hours than the probable heating control in the building-scale. Moreover, the accuracy of the model estimation is also improved to CV RMSE 5.6%.

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Section: Discussionmentioning

confidence: 99%

An energy model of high-rise apartment buildings integrating variation in energy consumption between individual units

Jang

Kang

2018

Energy and Buildings

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“…Reduced-order grey box models can behave better, but the estimation of parameters should be continuously updated [9,11]. For example Heidarinejad et al [12] developed effective urban-scale reduced-order building energy models by checking the most influent variables for the heat transfer process. Kontokosta and Tull [13], instead, developed a data-driven predictive model for energy use in buildings at urban scale.…”

Section: Scalabilitymentioning

confidence: 99%

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

Arteconi

2018

Environments

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The urgent need for the reduction of greenhouse gas (GHG) emissions requires efficient and integrated energy communities in order to decrease final energy demand and sustain energy transitions towards renewable energy sources (RES). This introduces a big research challenge for future energy systems design and optimization, given the multi-level and inter-sectorial dimensions of the problem. Buildings cover a central role in this context, because they represent the nexus between different energy carrier networks. The study of the management of multi-sector and multi-energy systems presents several challenges, such as the need to take into account uncertainties, to represent the heterogeneity of the energy demand and the scalability of the problem. This paper aims at outlining such a framework, and at showing the state-of-the-art nature of the above-mentioned challenges.

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“…Felsmann used reduced order building energy system modeling, e.g., district heating or cooling systems, to create largescale urban energy simulations [35]. Heidarinejad et al developed a framework to rapidly create urban scale reduced-order building energy models relying on the contributions of different influential variables to the internal, external, and system thermal loads [36]. Then the framework was validated by applying typical building geometries for simulations [36].…”

Section: Introductionmentioning

confidence: 99%

“…Heidarinejad et al developed a framework to rapidly create urban scale reduced-order building energy models relying on the contributions of different influential variables to the internal, external, and system thermal loads [36]. Then the framework was validated by applying typical building geometries for simulations [36]. Zhao [37].…”

Section: Introductionmentioning

confidence: 99%

Incorporating machine learning with building network analysis to predict multi-building energy use

Wang

Hong

et al. 2019

Energy and Buildings

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Predicting energy use of campuses or city district buildings has recently gained more attention due to dynamic large-scale building energy demands. This data enlightens public's awareness of energy use and informs building energy policy. Understanding the correlation of energy use patterns between buildings is a key issue to analyzing multi-building energy use. Moreover, how to apply this inter-building relationship to multi-building energy prediction, using significantly less amount of building energy data, is still an open question. To solve such problems, this study proposed an interdisciplinary research method to predict multi-building energy use by integrating a social network (SN) analysis with an Artificial Neural Network (ANN) technique. The SN method was used to identify reference buildings and determine correlations between reference buildings and non-reference buildings.The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SN-ANN method, 17 buildings in the Southeast ， Corresponding author. tel.: +86 (25) 8379-5689; xxdseu@126.com (Xiaodong Xu) ， Corresponding author. tel.: +1 (510) 486-7082; thong@lbl.gov (Tianzhen Hong) University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data, and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SN-ANN method achieved an accuracy of 90.28% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.

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Demonstration of reduced-order urban scale building energy models

Cited by 43 publications

References 43 publications

An energy model of high-rise apartment buildings integrating variation in energy consumption between individual units

An energy model of high-rise apartment buildings integrating variation in energy consumption between individual units

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

Incorporating machine learning with building network analysis to predict multi-building energy use

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