Rethinking the TMY: is the ‘Typical’ Meteorological Year Best for Building Performance Simulation?

Crawley, Drury B.; Lawrie, Linda K.

doi:10.26868/25222708.2015.2707

Cited by 34 publications

(18 citation statements)

References 8 publications

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“…Os modelos computacionais foram desenvolvidos com o uso de duas ferramentas: o Sketchup 2017 (versão 17.2.2555 64-bit) e o OpenStudio (versão 2.9.0), que seguem os algoritmos de cálculo do software EnergyPlus. As simulações foram realizadas com o arquivo climático EnergyPlus Weather Format (EPW) (Crawley;Lawrie, 2019), para a cidade de Formosa-GO (Latitude 15.533° e Longitude 47.333°).…”

Section: Desenvolvimento Dos Modelos Computacionaisunclassified

Natural ventilation

Teixeria Angueth de Araujo,

De Luna Sales

2024

Paranoá

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A ventilação natural, enquanto estratégia passiva, visa proporcionar ambientes com conforto e salubridade, atrelados à redução do consumo de energia. Diante disso, o objetivo dessa pesquisa é analisar a influência da ventilação natural no desempenho térmico de uma edificação residencial multifamiliar e o seu impacto no consumo energético. Os procedimentos metodológicos contemplam simulações computacionais com o uso do software EnergyPlus para avaliar o percentual de horas de conforto e a quantidade de renovações de ar por hora dos ambientes de permanência prolongada da edificação. Os resultados mostram que o sombreamento das aberturas associado a uma redução do tamanho das esquadrias apresenta influência positiva quanto ao conforto térmico. Além disso, mesmo com redução da área útil de ventilação, a qualidade do ar foi mantida em todos os ambientes analisados. Quanto à eficiência energética, o percentual de conforto encontrado para o melhor cenário corresponde a uma redução de 67,9% no consumo de energia e representa uma possibilidade de conforto sem o uso de aparelhos de condicionamento de ar.

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Section: Desenvolvimento Dos Modelos Computacionaisunclassified

Natural ventilation

Teixeria Angueth de Araujo,

De Luna Sales

2024

Paranoá

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“…Adoption of weather files encompassing the event (historical or projected future) [65][66][67] Urban heat island Adoption of weather files with variables measured onsite or adapted through tools that simulate the urban heat island effect [60,68] Power outages Modeling of power availability constraints [53,69] Occupants' physical limitations Modeling of building operation constraints Wildfires, air pollution, technical failure of building systems, or other events that affect building operation, especially those related to AC operation or the ability of opening windows…”

Section: Sources Of Stress In Building Performance Modelsmentioning

confidence: 99%

A simulation framework for assessing thermally resilient buildings and communities

Krelling,

Lamberts,

Malik

et al. 2023

Building and Environment

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“…In this and other locations, the XMYs developed using this method either did not bracket the range of https://doi.org/10.26868/25222708.2019.210594 Figure 1. Medium Office Building using Washington Dulles AP Weather Based on Selecting Extreme Months for XMYs (Crawley and Lawrie 2015) the results for individual years or were far beyond what was see in the individual years. The monthly XMYs with the greatest difference in energy use between min and max were dry-bulb temperature, dew-point temperature, and precipitation.…”

Section: Methodsmentioning

confidence: 86%

“…In a 2015 study (Crawley and Lawrie 2015), we selected an initial set of variables including dry-bulb temperature, dew-point temperature, solar insolation, precipitation, relative humidity, and wind speed. Then we evaluated these variables against the period of record: monthly extreme (average and daily), seasonal extreme, and single year.…”

Section: Methodsmentioning

confidence: 99%

“…We used the same weights used to create the IWEC (ASHRAE 2001): dry bulb mean: 30%, daily total solar: 40%, then dry bulb max/min: 5%, wind speed max/min: 5% and dew point max/min 2.5%. As described by Crawley and Lawrie (2015), these were the variables that best describes the inter-annual variation. For these five locations, we created two TMYs: one which covered the entire period of period (Dulles 55 years, Heathrow 55 years, Denver 23 years, Montreal 44 years, Singapore 39 years) and a second TMY, which was derived from the last 15 years of data.…”

Section: New Xmy Months Selection Methods Testedmentioning

confidence: 99%

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Should We Be Using Just ‘Typical’ Weather Data in Building Performance Simulation?

Crawley¹,

Lawrie²

Building Simulation Conference Proceedings

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Over the past 40 years, organizations worldwide have created weather data sets specifically for use in building energy simulation, usually called typical or reference years. Crawley (1998) showed how a variety of typical data sets compare in terms of impacts on building energy. This study found that TRY-type files (single years) do not represent the period of record well and recommends TMY or other weather data created using similar procedures, such as European test reference years. Several other studies have concluded that TMY are good enough to represent typical building operation. Yet we need weather that represents a reasonable range of climate conditions that buildings experience. A 2015 study proposed development of eXtreme Meteorological Year (XMY) weather files to represent the range of climate conditions that buildings may experience. An XMY starts with the same period of record as the TMY, but the methodology purposely selects more extreme months. This paper proposes a new regime for climatic data representation in buildings-an XMY or eXtreme Meteorological Year. We demonstrate how several sets of international typical meteorological data sets compare to the actual period of record that they represent. Then using prototype buildings, we show that the climatic response of the building would be better served by a range of building climatic data, investigating high and low cases of temperature, humidity, solar radiation and wind conditions.

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Rethinking the TMY: is the ‘Typical’ Meteorological Year Best for Building Performance Simulation?

Cited by 34 publications

References 8 publications

Natural ventilation

Natural ventilation

A simulation framework for assessing thermally resilient buildings and communities

Should We Be Using Just ‘Typical’ Weather Data in Building Performance Simulation?

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