Evaluation of weather datasets for building energy simulation

Bhandari, Mahabir; Shrestha, Som; Im, Piljae

doi:10.1016/j.enbuild.2012.01.033

Cited by 102 publications

(52 citation statements)

References 10 publications

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“…Eventually, typical years are also necessary for assessing the building energy performance under standard weather reference conditions, which are expected to be representative of the multi-year series in a given location. Some previous studies observed that the variability of buildings annual energy uses are less than 10 % in the multi-year period -between 4 % and 6 % for U.S. climates [8,9] or 4.6 % for Hong-Kong [10]. Although the previous studies are valid only for the climatic context and buildings analysed, they indicate that a single reference year can generally be used to express the typical energy performance.…”

Section: Introductionmentioning

confidence: 90%

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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Section: Introductionmentioning

confidence: 90%

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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“…Significant variations in simulated energy use from the two different weather periods were found and weather data covering more recent periods were recommended to be used for better prediction of actual energy use in buildings. Bhandari et al [19] studied the quality of weather data from two different sources by comparing them to actual measured weather data, and the associated impact on building cooling and heating loads and energy consumption for a single year at a specific U.S. location.…”

Section: Discrepancies Of Weather Data From Different Sources and Difmentioning

confidence: 99%

“…Accurate estimation of building performance relies on the appropriate selection of accurate weather data. The quality of weather data and their impact on building cooling and heating loads and energy consumption were studied by comparing three weather datasets for a specific location for the calendar year 2010 [19]. The three sources of data included site measured data and AMY weather data provided by two vendors.…”

Section: Introductionmentioning

confidence: 99%

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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“…In this particular case, the database used for the training of the feedforward neural network is based on the study done by the Oak Ridge National Laboratory, located in Oak Ridge, Tennessee [18]. This study analyzed the meteorological factors of dry bulb temperature, global horizontal irradiance, wind speed, dew point temperature, wind direction and relative humidity for the year 2010, and their relation with the energy consumption of a building.…”

Section: Methodsmentioning

confidence: 99%

Estimated Energy Consumption for a Building Based on Weather and Time Conditions Using Neural Networks

Marrugo¹,

Ospina²,

Amaya

2016

IJCA

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Evaluation of weather datasets for building energy simulation

Cited by 102 publications

References 10 publications

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

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

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

Estimated Energy Consumption for a Building Based on Weather and Time Conditions Using Neural Networks

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