Residential end-use energy simulation at city scale

Shimoda, Yoshiyuki; Fujii, Takuro; Morikawa, Takao; Mizuno, Minoru

doi:10.1016/j.buildenv.2004.01.020

Cited by 151 publications

(70 citation statements)

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“…The house archetype approach has been used by a number of authors to model energy and resource quantities and impacts, from a study at a regional level by Lechtenböhmer and Schüring [11] to more recent studies at urban scales by Firth et al [12] and Shimoda et al [13]. The emergence of many energy and resource reduction models driven by the need to support the 4 assessment of emissions mitigation policies in the UK residential sector has been demonstrated by the BREHOMES model [14], the 40% house project [15] and the model developed by Johnston et al (henceforth referred to as the Johnston model) [16].…”

Section: Existing Archetype Development Methodologiesmentioning

confidence: 99%

Developing archetypes for domestic dwellings—An Irish case study

Famuyibo¹,

Duffy²,

Strachan

2012

Energy and Buildings

112

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Section: Existing Archetype Development Methodologiesmentioning

confidence: 99%

Developing archetypes for domestic dwellings—An Irish case study

Famuyibo¹,

Duffy²,

Strachan

2012

Energy and Buildings

112

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“…In Canada, Canadian Residential Energy End-use Model (CREEM), developed by Farahbakhsh et al (1998), was based on 16 house archetypes that were derived from 8,767 actual houses' data. In Japan, the residential energy model developed by Shimoda et al (2003) used 20 archetypes and 23 household types for Osaka City's residential sector. The occupants' schedule determined by the national time use survey was utilized to obtain hourly energy use for hot water supply and dynamic cooling and heating load.…”

Section: Existing Building Stock Energy Prediction Modelsmentioning

confidence: 99%

“…Total energy consumption for the residential stock was estimated by multiplying the simulated energy use and the number of households and then summing them up. This concept of stock modeling has been developed and employed in a series of research (Shimoda et al 2004(Shimoda et al , 2010aYamaguchi et al 2008). These residential building stock models have core simulation engines tailored for residential buildings and specific regions.…”

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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“…In this study, the family distribution and type of housing were considered as variables, as well as other factors related to the use of housing, climatic characteristics, among others. Along with the above, it was pointed out that through these types of tools, the results of actual energy policies can be evaluated, as well as modify the scenarios, internal loads and varying occupancy parameters, in order to predict the future energy changes (Shimoda, Fujii, Morikawa, & Mizuno, 2004). …”

Section: State Of the Artmentioning

confidence: 99%

Residential archetypes in urban energy simulation models in Chile: Determining factors of residential energy consumption

Aguilera

Ossio

2017

rdlc

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The national and international overview has shown that residential energy consumption is increasing. As a result, efforts have focused on reducing the initial demand for housing and its associated consumption. One of the most used tools for this purpose is bottom-up energy modelling, specifically on the archetypes branch. This method proposes that energy demand of a dwelling can estimated from the interaction of several parameters and factors. However, the method's main limitation, is the complexity of defining the variables and factors to be used. This article presents a review of specialized literature, concerned with, determining and characterizing residential archetypes. From this literature, It was possible to determine the most influential factors in energy consumption. The results showed 19 factors that were categorized into five groups; general, geometric, thermal, operational, and systems and equipment characteristics. The applicability of these factors to Chile was verified. This article, is intended a guide to generating representative archetypes and characterizing dwellings in an efficiente manner.Key words: energy efficiency, bottom-up modelling, archetypes, residential energy consumption, urban energy simulation. ResumenEl panorama nacional e internacional han demostrado que el consumo energético residencial está en alza. Por lo cual, los esfuerzos se han centrado en disminuir la demanda inicial de las viviendas y su consumo asociado. Para esto, una de las herramientas más utilizadas corresponde metodología bottom-up, específicamente del tipo arquetipos. Esta propone, que, a través de la interacción de diversos parámetros y factores, se puede obtener demanda energética de una vivienda, generando así arquetipos representativos del stock de viviendas simuladas, el cual se puede extrapolar a distintos niveles. Sin embargo, la principal limitante, corresponde a la complejidad de definir las variables y factores a utilizar. Este artículo presenta una revisión de literatura especializada, donde se compilaron las variables, más utilizadas en el consumo energético, para determinar y caracterizar arquetipos residenciales. Se obtuvieron 19 variables las que se categorizaron en cinco niveles; características generales, geométricas, térmicas y operacionales, y de equipamiento y sistemaas, a las que, se les verificó su aplicabilidad en Chile. A través de este artículo se pretende generar una guía que permita realizar una caracterización eficiente de las viviendas y su contextualización, de manera que los arquetipos que se puedan generar sean lo más representativos posibles.Palabras Claves: eficiencia energética, modelación bottom-up, arquetipos, consumo energético residencial, simulación energética urbana.

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Residential end-use energy simulation at city scale

Cited by 151 publications

References 1 publication

Developing archetypes for domestic dwellings—An Irish case study

Developing archetypes for domestic dwellings—An Irish case study

Review on stochastic modeling methods for building stock energy prediction

Residential archetypes in urban energy simulation models in Chile: Determining factors of residential energy consumption

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