David A. Coley

On the creation of future probabilistic design weather years from UKCP09

Weather data are used extensively by building scientists and engineers to study the performance of their designs, help compare design alternatives and ensure compliance with building regulations. Given a changing climate, there is a need to provide data for future years so that practising engineers can investigate the impact of climate change on particular designs and examine any risk the commissioning client might be exposed to. In addition, such files are of use to building scientists in developing generic solutions to problems such as elevated internal temperatures and poor thermal comfort. With the publication of the UK Climate Projections (UKCP09) such data can be created for future years up to 2080 and for various probabilistic projections of climate change by the use of a weather generator. Here, we discuss a method for the creation of future probabilistic reference years for use within thermal models. In addition, a comparison is made with the current set of future weather years based on the UKCIP02 projections. When used within a dynamic thermal simulation of a building, the internal environments created by the current set of future weather files lie within the range of the internal environments created by the probabilistic reference years generated by the weather generator. Hence, the main advantages of the weather generator are seen to lie in its potentially greater spatial resolution, its ability to inform risk analysis and that such files, unlike ones based on observed data, carry no copyright. Practical applications: The methodology presented in this article will allow academics and buildings engineers to create realistic hourly future weather files using the future climate data of UKCP09 weather generator. This will allow the creation of consistent future weather years for use in areas such as building thermal simulation.

Low-energy design: combining computer-based optimisation and human judgement

Abstract Simply minimising the heat loss from a building will not necessarily lead to an exemplary low-energy design: overheating may occur, leading to a large amount of cooling energy being used, and the shape and form of the design may not fit with other sensitivities and elements of the design brief. This paper couples a population-based optimisation algorithm (a genetic algorithm) to a dynamic thermal model with the idea of identifying large numbers of distinctly different low-energy designs. These designs are then presented to the user in the form of a visual summary for judgement as to potential use. In order that sufficiently different designs are evolved, and the thermal model can be run over a complete year on an hourly grid, several adaptations to the genetic algorithm have had to be made. The approach is illustrated by the design of a community hall. An extensive range of design possibilities is identified which achieve low-energy status by greatly different means with some concentrating on reducing losses and others on maximising their use of causal gains, including solar gains.

Food miles: time for a re‐think?

Purpose – The purpose of this paper is to test the efficacy of the concept of food miles that has proved so popular with the public as a means of assessing the sustainability of produce.Design/methodology/approach – This paper uses data from a UK major food importer and retailer to correlate carbon emissions from transport, and transport‐related storage, with food miles by creating farm‐specific mode‐weighted emission factors.Findings – The correlation is found to be poor for a wide range of products and locations and it is clear that the mode of transport is as important as the distance, with sourcing from parts of the Mediterranean resulting in emissions greater than those from the Americas.Practical implications – It is concluded that it is difficult to justify the use of food miles when attempting to influence purchasing behaviour. Because of this result, processes and tools have been developed that relay information on true transport‐related carbon emissions to customers and bulk purchasers that allo...

The Effect of Low Ventilation Rates on the Cognitive Function of a Primary School Class

Abstract Several studies have suggested that recommended ventilation rates are not being met within schools. However these studies have not included an evaluation of whether or not this failure might have an impact on pupil performance and learning outcome. The work reported here was designed as an initial investigation into this question. Using the Cognitive Drug Research computerised assessment battery to measure cognitive function, this study demonstrates that the attentional processes of school children are significantly slower when the level of CO2 in classrooms is high. The effects are best characterised by the Power of Attention factor which represents the intensity of concentration at a particular moment, with faster responses reflecting higher levels of focussed attention. Increased levels of CO2 (from a mean of 690 ppm to a mean of 2909 ppm) led to a decrement in Power of Attention of approximately 5%. Thus, in a classroom where CO2 levels are high, students are likely to be less attentive and to concentrate less well on what the teacher is saying, which over time may possibly lead to detrimental effects on learning and educational attainment. The size of this decrement is of a similar magnitude to that observed over the course of a morning when students skip breakfast.

Estimation of the urban heat island for UK climate change projections

Cities are known to exert a significant influence on their local climate, and are generally warmer than their surroundings. However, climate models generally do not include a representation of urban areas, and so climate projections from models are likely to underestimate temperatures in urban areas. A simple methodology has been developed to calculate the urban heat island (UHI) from a set of gridded temperature data; the UHI may then be added to climate model projections and weather data files. This methodology allows the UHI to be calculated on a monthly basis and downscaled to hourly for addition to weather generator data. The UHI intensities produced are found to be consistent with observed data. Practical application: There is overwhelming consensus amongst the scientific community that the Earth’s climate is warming. In addition to the effects of climate change the urban heat island (UHI) effect can increase air temperatures significantly in urban areas above those of the rural areas around them. The proposed methodology for calculating the UHI from a set of gridded temperature data allows the UHI to be added to climate model projections such as UKCP09 or HadRM3 and weather data files. The methodology also allows for the temporal downscaling of the UHI from monthly values to hourly data for use in building thermal simulation software.

Comparison of multi-year and reference year building simulations

Buildings are generally modelled for compliance using reference weather years. In the UK these are the test reference year (TRY) used for energy analysis and the design summer year (DSY) used for assessing overheating in the summer. These reference years currently exist for 14 locations around the UK and consist of either a composite year compiled of the most average months from 23 years worth of observed weather data (TRY) or a single contiguous year representing a hot but non-extreme summer (DSY). In this paper, we compare simulations run using the reference years and the results obtained from simulations using the base data sets from which these reference years were chosen. We compare the posterior statistic to the reference year for several buildings examining energy use, internal temperatures, overheating and thermal comfort. We find that while the reference years allow rapid thermal modelling of building designs they are not always representative of the average energy use (TRY) exposed by modelling with many weather years. Also they do not always give an accurate indication of the internal conditions within a building and as such can give a misleading representation of the risk of overheating (DSY). Practical applications: An understanding of the limitations of the current reference years is required to allow creation of updated reference years for building simulation of future buildings. By comparing the reference years to the base data sets of historical data from which they were compiled an understanding of the benefit of multiple simulations in determining risk can be obtained.

An artificial intelligence approach to the prediction of natural lighting levels

Abstract Controlling artificial lights within buildings to act solely as a supplement to available daylighting requires continuous knowledge of natural lighting levels within a space. Although this information is readily obtained by measurement whilst lights are extinguished, once illuminated the determination of the underlying natural light level is not so straightforward. This paper describes the use of a Genetic Algorithm (GA) at the heart of a self-commissioning, adaptive algorithm capable of the real-time prediction of natural light levels at chosen points within a room using external measurements of vertical plane illuminance. The algorithm is extremely compact and efficient and readily implemented on a microprocessor. As such, it is suggested that it could form the basis of a robust and practical lighting controller.

Carbon Dioxide Levels and Summertime Ventilation Rates in UK Schools

Abstract Measurements of metabolic carbon dioxide concentration made in four classrooms in two schools are reported for both occupied and unoccupied periods. Measurements were taken for approximately one week in each classroom during the unheated season and the time-varying ventilation rates estimated. The results of the experiments show CO2 concentrations that are far beyond the guideline value of 1000 ppm (the maximum concentration during the occupied period was 3756 ppm). Calculated air supply rates vary from unacceptably low levels, to rates that are in line with guidance. The occurrence of periods with acceptable supply rates, and the rates found during purge ventilation, show that the surveyed classrooms have the potential to provide adequate fresh air. Inaccessible windows and ventilation openings, combined with lack of guidance on when and how to apply ventilation seems to be the primary reason for poor ventilation outside the heating season.

The appropriate spatial resolution of future weather files for building simulation

Building thermal modelling packages require weather data to predict representative internal conditions. Typically, around the world, reference weather years of various forms are used which are created from observations at aparticular location. However, it is unlikely that this location is identical to that of the building. This can lead toweather files for coastal locations being applied to inland and upland sites or vice versa. In the UK, the UKCP09 weather generator has the ability to produce weather at a 5 km resolution. Currently, it is unclear how useful this extra spatial resolution will be and it is this question that is addressed here. It is found that for both future and present climate, the spatial variability of the weather is the dominating factor. Although there are geographies where a low spatial resolution can be used, there are regions where a much higher resolution is necessary.

The creation of wind speed and direction data for the use in probabilistic future weather files

Pseudo weather data with a high temporal resolution are of use in many fields including the modelling of agricultural systems, the placement of wind turbines and building thermal simulations. With the publication of the 2009 UK Climate Projections (UKCP09) such data can be created for future years and for various predictions of climate change. Unfortunately such — UKCP09 — data does not include information about wind speed or direction due to a lack of robustness. Here we demonstrate a methodology for generating such wind data on an hourly time grid from a consideration of the potential evapotranspiration reported by the UKCP09 weather generator and information related to the correlation between observed wind speed, direction and time of year. We find our pseudo wind data is consistent with the historic observed wind. Furthermore, when used within a dynamic thermal simulation of a building, the use of such pseudo wind data generates a consistent internal environment in terms of ventilation rates, temperatures and energy use that is indistinguishable from simulations completed using historic observed weather for both single-sided and cross-ventilated buildings. Practical applications: The methodology presented in this paper will allow academics and building engineers to create realistic hourly wind speed and direction data for inclusion with the future climate data of UKCP09. This will allow the creation of consistent future weather years for use in areas such as building thermal simulation.