Analyses and algorithms for new Test Reference Years and Design Summer Years                 for the UK

Levermore, Geoff; Parkinson, John

doi:10.1177/0143624406071037

Cited by 91 publications

(128 citation statements)

References 3 publications

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“…The Test Reference Year (TRY), for example, is composed of 12 separate months of data each one chosen to be the most average month among a set of base years [16]. The cumulative distribution functions on which the TRY is based are made up of the daily mean values of three parameters: dry bulb temperature, cloud cover (used as a proxy for solar irradiation), and wind speed.…”

Section: Representative Weathermentioning

confidence: 99%

“…Component months are chosen using the Finkelstein-Schafer (FS) statistic method, essentially, those months with the most average values of temperature, radiation and wind speed combined. In the case of the TRY, each of the 3 environmental parameters carries an equal weighting; this was deemed an appropriate method for naturally ventilated buildings [16].…”

Section: Representative Weathermentioning

confidence: 99%

“…5 By contrast, the Design Summer Year (DSY) [16] is primarily an attempt to estimate the impact of warmer than average summers. It was initially intended for the sizing of mechanical cooling systems and is now used for assessing the risk of overheating in naturally ventilated buildings.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Probabilistic adaptive thermal comfort for resilient design

Coley

Herrera

Fosas

et al. 2017

Building and Environment

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Adaptive thermal comfort theory has become the bedrock of much thinking about how to judge if a free-running environment is suitable for human occupation. In design work, the conditions predicted by a thermal model, when the model is presented with one possible annual weather time series (a reference year), are compared to the limits of human comfort. If the temperatures are within the comfort limits, the building is judged to be suitable. However, the weather in many locations can vary year-on-year by a considerable margin, and this begs the question, how robust are the predictions of adaptive comfort theory likely to be over the many years a building might be in use? We answer this question using weather data recorded for up to 30 years for locations within each of the five major Köppen climate classifications.We find that the variation in the annual time series is so great that the predicted comfort temperature frequently lies outside the acceptable range given by the reference year. Return periods for the excursions of the time series are calculated for each location. The results for one location are then validated using the world's longest temperature record. These results suggest that industry and academia would be best advised to move to a probabilistic methodology, like the proposed one, when using adaptive comfort theory to judge the likely conditions within a building. Extra pertinence is provided by concerns over increases in mortality and morbidity in buildings due to a rapidly warming climate. • A new probabilistic adaptive comfort theory introduced.• New comfort equations for resilient building design presented.

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Section: Representative Weathermentioning

confidence: 99%

Section: Representative Weathermentioning

confidence: 99%

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Probabilistic adaptive thermal comfort for resilient design

Coley

Herrera

Fosas

et al. 2017

Building and Environment

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“…The 12 selected typical months for each station were chosen using the Finkelstein-Schafer statistic method [4], which considering the combination of global horizontal solar radiation, dry bulb temperature, direct normal solar radiation, wind speed and dew point temperature, which are the important variables for building performance simulation.…”

Section: A Weather Data For Building Performance Predictionmentioning

confidence: 99%

Understanding the reliability of localized near future weather data for building performance prediction in the UK

Jones

2016

2016 IEEE International Smart Cities Conference (ISC2)

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Abstract-Access to reliable site-specific near future weather data is crucial for forecasting temporally-dynamic building energy demand and consumption, and determining the state of on-site renewable energy generation. Often there is a missing link between weather forecast providers and building energy management systems. This short paper discusses the potential to conduct building performance modelling using localized high resolution weather forecast freely available from the United Kingdom Met Office DataPoint service. It creates a great opportunity for building performance simulation professionals and building energy managers to re-use site-specific high resolution weather forecast data to predict near future building performance at both individual building and city scale. In this paper, authors have developed a framework of forecasting near future building performance and a Matlab script to automatically gather observed weather data from 140 weather stations and weather forecasts for nearly 6,000 locations in the UK. To understand the reliability of weather forecast, threehourly forecasts of temperature, relative humidity, wind speed and wind direction are compared with observations from weather stations. This provides evidences to use the next 24-hour forecast to predict dynamic building energy demand and consumption, and determine the on-site renewable energy generation output. Because of the high accuracy of forecast, the rolling forecast can be recorded on daily basis to construct weather files for locations that do not have weather stations. This will increase current 14 locations of the CIBSE weather data to nearly 6,000 locations covering population centers, sporting venues and tourist attractions.

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“…TRYs are typically compiled from 20+ years of historical measured data (typically 1983 to 2004) which is then sorted by weighting key variables in order to create a composite year from the most typical individual months. The mathematical basis for this procedure can be found in Levermore and Parkinson [34]. When TRY weather files are produced they are compiled from representative months and the Finkelstein-Schafer (FS) statistic is commonly used to select the most average months.…”

Section: 5mentioning

confidence: 99%

A proposed method for generating high resolution current and future climate data for Passivhaus design

McLeod

Hopfe

Rezgui

2012

Energy and Buildings

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The sensitivity of low energy and passive solar buildings to their climatic context creates a requirement for accurate local climate data. This situation takes on increasing importance in the context of modelling Passivhaus buildings where the absence of conventional oversized heating and cooling systems implies a greater reliance upon fabric and system optimisation. Conversely, future climatic changes may also pose serious implications for super insulated buildings with inadequate solar shading. Currently, many widely used building performance simulation (BPS) tools still rely on very limited sources of climate data.The following research examines the need for regional and, in some cases, micro-regional climatic data when designing ultra-low energy Passivhaus buildings in the UK. The paper proposes a new methodology for generating this data in PHPP format. The data generated is compared to alternative sources, and the implications discussed in the context of three case studies examining a certified Passivhaus dwelling in a mountainous region of Wales as well as two locations, in close proximity, within London. If correctly implemented the use of such data should provide a more robust basis for future cost and performance optimisation in low energy and passive building design.

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Analyses and algorithms for new Test Reference Years and Design Summer Years for the UK

Cited by 91 publications

References 3 publications

Probabilistic adaptive thermal comfort for resilient design

Probabilistic adaptive thermal comfort for resilient design

Understanding the reliability of localized near future weather data for building performance prediction in the UK

A proposed method for generating high resolution current and future climate data for Passivhaus design

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