Building energy simulation using multi-years and typical meteorological years in different climates

Yang, Linlin; Lam, Joseph C.; Liu, Jiaping; Tsang, C.L.

doi:10.1016/j.enconman.2007.05.004

Cited by 95 publications

(29 citation statements)

References 9 publications

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“…However, it becomes important to select a particular standard weather year data set when absolute energy consumption data are required. Similar studies on office buildings were conducted in five major climate zones in China by using multi-year weather databases as well as TMY data [16][17][18]. The results showed a decreasing trend for heating loads and an increasing trend for cooling loads due to predicted climate change.…”

Section: Introductionmentioning

confidence: 83%

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

2013

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Buildings consume more than one third of the world's total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980 to 2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: 1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; 2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; 3) the weather impact is greater for buildings in colder climates than warmer climates; 4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and 5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.

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Section: Introductionmentioning

confidence: 83%

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

2013

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“…Yang et al [7]. This does not represent the global error but just the effect of the inaccuracy in the modelling of the weather data and it has to be summed to the uncertainties of all other inputs and to the effects of the modelling assumptions.…”

Section: B Building Energy Performancesmentioning

confidence: 99%

“…The representative days are hourly data for some average days descriptive of the typical climatic conditions (e.g., summer conditions). Simulations with typical years (or representative days) instead of multi-year weather data lead to less information but they are less time-consuming and results are easier to manage [6,7]. They are also preferred to mitigate the effects of missing or wrong data in the collected series.…”

Section: Introductionmentioning

confidence: 99%

Multi-year and reference year weather data for building energy labelling in north Italy climates

Pernigotto

Prada

Cóstola

et al. 2014

Energy and Buildings

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“…x 4 +a 10 + a 11 (2) where a 1 -a 11 are regression model coefficients, and x 1 -x 4 are the surface net shortwave radiation, ambient air temperature, relative humidity, and wind velocity, respectively. The values of a 1 -a 11 were obtained via regression analysis using OriginPro, and the statistical model for calculation of the evaporation rate is shown in Eq.…”

Section: Multivariate Nonlinear Model Of Evaporation Ratementioning

confidence: 99%

“…Owing to the hot-humidity climate [1,2] and rapid improvement of living standards [3], the amount of space-cooling equipment in use has increased dramatically over the last 10 years. At the end of 2014, the number of air conditioners owned per 100 households in Guangzhou was 220 units [3], almost three times the national average [4].…”

Section: Introductionmentioning

confidence: 99%

The impact of evaporation from porous tile on roof thermal performance: A case study of Guangzhou’s climatic conditions

Zhang

et al. 2017

Energy and Buildings

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a b s t r a c tThe evaporative-cooling roof is a popular passive energy conservation technique. This article presents a novel approach for modelling and analysing the influence of evaporation on roof thermal performance. A multivariate nonlinear model was developed for the prediction of the evaporation rate from porous tile. A computer program was then developed based on the one-dimensional roof unsteady heat transfer theory. Finally, the computer program and hourly weather data of Guangzhou, China, were used to analyse the impacts of evaporation (including the evaporation start time and water-application frequency) and slope orientation on roof thermal performance. Evaporation beginning at 11:00 can reduce the external surface temperatures of a horizontal roof and 30 • -inclined east-sloping and west-sloping roofs by up to 11.3, 10.7, and 9.8 • C, respectively, from those of a non-evaporative roof. This, in turn, can reduce the peak-hour (16:00-20:00) internal surface heat flux. Additionally, with the horizontal roof, the reduction in the peak-hour heat flux can be doubled if the evaporative layer is frequently replenished with water.

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Building energy simulation using multi-years and typical meteorological years in different climates

Cited by 95 publications

References 9 publications

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

Multi-year and reference year weather data for building energy labelling in north Italy climates

The impact of evaporation from porous tile on roof thermal performance: A case study of Guangzhou’s climatic conditions

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