Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City

Cerezo, Carlos; Sokol, Julia; AlKhaled, Saud; Reinhart, Christoph; Al-Mumin, Adil; Hajiah, Ali

doi:10.1016/j.enbuild.2017.08.029

Cited by 122 publications

(56 citation statements)

References 39 publications

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“…The interaction of occupants with the building is related to their presence, movement, and actions, such as opening windows, switching lights, adjusting blinds, and the use of appliances. Usually, in UBEM each typology of buildings is modeled via an archetype based on the year of construction and its intended use [94]. These archetypes include the characteristics of the envelope, systems and the schedules of typical OB.…”

Section: 2mentioning

confidence: 99%

Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches

Ferrando

Causone

Hong

et al. 2020

Sustainable Cities and Society

195

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Regulations corroborate the importance of retrofitting existing building stocks or constructing new energy-efficient districts. There is, thus, a need for modeling tools to evaluate energy scenarios to better manage and design cities, and numerous methodologies and tools have been developed. Among them, Urban Building Energy Modelling (UBEM) tools allow the energy simulation of buildings at large scales. Choosing an appropriate UBEM tool, balancing the level of complexity, accuracy, usability, and computing needs, remains a challenge for users. The review focuses on the main bottom-up physics-based UBEM tools, comparing them from a user-oriented perspective. Five categories are used: (i) the required inputs, (ii) the reported outputs, (iii) the exploited workflow, (iv) the applicability of each tool, and (v) the potential users. Moreover, a critical discussion is proposed, focusing on interests and trends in research and development. The results highlighted major differences between UBEM tools that must be considered to choose the proper one for an application. Barriers of adoption of UBEM tools include the needs of a standardized ontology, a common threedimensional city model, a standard procedure to collect data, and a standard set of test cases. This feeds into future development of UBEM tools to support cities' sustainability goals.

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Section: 2mentioning

confidence: 99%

Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches

Ferrando

Causone

Hong

et al. 2020

Sustainable Cities and Society

195

View full text Add to dashboard Cite

show abstract

“…Moreover, Cerezo et al [24] proposed a Bayesian calibration technique to determine the frequency distributions for occupancy, lighting power density, plug load density, and cooling temperature setpoints.…”

Section: Related Work and Knowledge Gapsmentioning

confidence: 99%

“…The full combination of the parameters leads to a massive number of simulations. In the previous UBEM calibration studies, 100 samples [24], 200 to 400 samples [22], and 1000 samples [23] were generated for each building. So, the is the end use result of the reference building for the sample, which is stored in the energy performance database., the end-use results are guessed and aggregated to generate the annual electricity and natural gas EUIs using equation (1).…”

Section: Calibrate the Ubemmentioning

confidence: 99%

Automatic and Rapid Calibration of Urban Building Energy Models by Learning from Energy Performance Database

Chen¹,

Hong²

2020

Preprint

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Urban building energy modeling (UBEM) is attracting increasing attention in the energy modeling filed. Unlike modeling a single building using detailed building systems information, UBEM generally uses limited high-level building stock data to infer default assumptions about building characteristics and operations. This practice inherently brings uncertainty to UBEM. This study introduced a novel method of automatic and rapid calibration of UBEM based on the annual electricity and natural gas energy use data by learning the correlations between crucial model input parameters and the building energy use from the reference building models. A case study was presented to calibrate 72 large office buildings built before 1978 in San Francisco. Seventeen model parameters were selected and Monte Carlo sampling was used to create 1000 samples that reasonably represent the parameter space. Then 1000 simulations were performed for the reference building model to create an energy performance database. The results showed that by learning from the energy performance database, it took less than four simulation runs on average to calibrate a building model. After the calibration, the distributions of each parameter were obtained to replace their single predefined default values. For example, the default lighting power density of 21.39 W/m 2 was calibrated to be 7.50 W/m 2 on average. The case study successfully demonstrated the effectiveness of the novel calibration method for UBEM in the mild climate. The method will be further tested in future for other climate zones and other building types.

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“…There is a relatively broad and mature field of study which extends and leverages building energy modeling software to enable performance simulation for energy efficien technologies in the context of whole-building operation. Even prior to the increasing pro liferation of urban-scale energy modeling studies, scalable application of technology measures in different building types, locations, and vintages for the purposes of building codes and standards was, and remains, a well-established use case that significantly im pacts long-term energy use of the building stock [2][3][4][5]. Such use cases typically involve hundreds to thousands of individual models.…”

Section: Simulation and Analysismentioning

confidence: 99%

“…Various UBEM analyses have been performed in different geographic areas and using different methodologies. A study of 332 residential buildings in Kuwait City introduced a Bayesian calibration method or archetype assignment with improved error rates compared to deterministic approaches [2]. A similar Bayesian approach was taken on 2663 buildings in Cambridge, Massachusetts, where annual and monthly calibration approaches were compared [3].…”

Section: Introductionmentioning

confidence: 99%

Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area

Bass

Copeland

2020

Energies

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Several companies, universities, and national laboratories are developing urban-scale energy modeling that allows the creation of a digital twin of buildings for the simulation and optimization of real-world, city-sized areas. Prior to simulation-based assessment, a baseline of savings for a set of utility-defined use cases was established to clarify the initial business case for specific energy efficient building technologies. In partnership with a municipal utility, 178,337 OpenStudio and EnergyPlus models of buildings in the utility’s 1400 km2 service area were created, simulated, and assessed with measures for quantifying energy, demand, cost, and emissions reductions of each building. The method of construction and assumptions behind these models is discussed, definitions of example measures are provided, and distribution of savings across the building stock is provided under a maximum technical adoption scenario.

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Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City

Cited by 122 publications

References 39 publications

Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches

Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches

Automatic and Rapid Calibration of Urban Building Energy Models by Learning from Energy Performance Database

Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area

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