Proposal of a modeling approach considering urban form for evaluation of city level energy management

Yamaguchi, Yohei; Shimoda, Yoshiyuki; Mizuno, Minoru

doi:10.1016/j.enbuild.2006.09.011

Cited by 102 publications

(42 citation statements)

References 11 publications

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“…Previous studies have estimated spatial distributions of urban energy consumption by scaling data from representative buildings with known usage to larger geographic scales (Brownsword et al, 2005;Heiple and Sailor, 2008;Yamaguchi et al, 2007). One geographically comprehensive study modeled building energy consumption, including heating, cooling, and other end-uses, across the boroughs of New York City (Howard et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity

et al. 2016

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Building energy use varies widely across metropolitan Los Angeles. Building age, household income, home ownership rates, and land use are all correlated with energy consumption. High-income areas use more energy per building, while lower-income areas use more energy per square-foot. Account-level energy use data can help local governments devise conservation strategies. Energy efficiency programs need evaluated using energy consumption data. a r t i c l e i n f o b s t r a c tResidential and commercial buildings comprise approximately forty percent of total energy consumption and carbon dioxide emissions in the U.S. Yet, while California spends $1.5 billion annually on energy efficiency programs, limited research has explored how building energy consumption varies within cities, including the social and structural factors that influence electricity and natural gas use. We present results from an analysis of aggregated account-level utility billing data for energy consumption across the over two million properties in Los Angeles County. Results show that consumption in L.A. County varies widely with geography, income, building characteristics, and climate. Several higher-income areas have greater total energy use per building even in cooler climates, while many lower-income regions rank higher for energy use per square-foot. Energy consumption also correlates with building age, which varies widely throughout the region. Our results demonstrate the many complex and interrelated factors that influence urban energy use. While billing data is critical for devising energy efficiency programs that actually realize estimated savings and promote more sustainable cities, opening access to such data presents significant challenges for protecting personal privacy. The presented approach is adaptable and scalable to cities seeking to develop data-driven policies to reduce building energy use.

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Section: Introductionmentioning

confidence: 99%

Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity

et al. 2016

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“…Yamaguchi et al (2007a) developed 612 archetypes for the commercial sector in Osaka city and proposed a district clustering approach that develops several representative districts based on dominant building types. The district level energy system simulation model was applied to estimate the annual energy use intensity of each representative district (Yamaguchi et al 2003(Yamaguchi et al , 2005(Yamaguchi et al , 2007bHashimoto et al 2007;Yamaguchi and Shimoda 2010).…”

Section: Existing Building Stock Energy Prediction Modelsmentioning

confidence: 99%

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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“…However this body of work is interesting in its attempt to integrate data on built form, energy end-use and activity types. Yamaguchi et al (2007) describe a model using a method called "district clustering" designed to identify archetypal urban areas, which can be aggregated to represent a city. It relies on surveys of larger buildings to establish the archetypes but the rationale for selecting areas and building is not entirely clear and is unlikely to translate well internationally.…”

Section: Previous Workmentioning

confidence: 99%

Enabling urban-scale energy modelling: a new spatial approach

Taylor

Fan²,

Rylatt³

2013

Building Research & Information

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Urban-scale energy modelling provides an ideal tool for studying nondomestic energy consumption and emissions reduction at the community level. In principle, an approach based on the characteristics of individual commercial premises and buildings is attractive but poses a number of challenges, the most immediate of which is deciding precisely what to model. For a range of reasons connected with their self-contained nature, individual non-domestic buildings would ideally be selected. However, the main information sources available, digital mapping and business taxation data, are not based on "buildings" and do not use the concept, thus making an automatic approach problematic. At the same time, manual identification of the distinct buildings in a city is not a practical proposition because of the numbers involved.The digital mapping and business taxation data are brought together in the local land and property gazetteer (LLPG). An analysis of the relationships between the relevant elements, namely building polygons and premises attracting business taxation, allowed a unit to be defined that matches the definition of a "building" in most circumstances and can be applied without the need for human intervention. This novel approach provides a firmer basis for modelling non-domestic building energy at the urban scale.(198 words) 3

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Proposal of a modeling approach considering urban form for evaluation of city level energy management

Cited by 102 publications

References 11 publications

Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity

Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity

Review on stochastic modeling methods for building stock energy prediction

Enabling urban-scale energy modelling: a new spatial approach

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