Building Services Engineering Research & Technology

In London, the urban heat island increases the average and peak air temperatures which in turn affect the demand for heating and cooling. To assess this, the simultaneous hourly air temperature in London has been measured continuously for a year at 80 locations, on a radial grid covering an area of 500 square miles. These have shown that central areas of London are significantly warmer than the surrounding areas (2 K warmer over the year). The measured data have been used as input to a thermal simulation model to assess the heating and cooling load of a standard air-conditioned office building positioned at different locations within the heat island. It is found that the urban cooling load is up to 25% higher over the year, and the annual heating load is reduced by 22%. Minimum CO₂ is emitted at a rural location. The net rate of increase of CO₂ with temperature is found to be 2.8%/K.

While it is relatively easy to describe how a change in electricity demand causes a change in carbon emissions from a system of grid-supplied power, it is by no means easy to quantify this. The following have all been used to determine the carbon emissions from changes in electricity demand:

• the system-average carbon intensity (that is, the ratio between all carbon emitted to all power distributed);

• the carbon intensity of the typical marginal plant; and

• the carbon intensity of new plant built or avoided.

All of these are valid in certain circumstances, but in others can give misleading results. This paper brings together existing information and new results of modeling a limited number of situations to propose, pro tem, a working set of figures for England and Wales, together with recommendation for their application. Further modelling and analysis is proposed to refine thesefigures.

Results of the ” rst studies speci” cally dedicated to the impact of climate change on the thermal behaviour of buildings in Portugal are discussed within this paper. A top-down econometric/statistical analysis was attempted in order to correlate ‘ uctuations of temperature and energy consumption in the residential and services sectors, but the results were not conclusive. A bottom-up numerical thermal simulation of representative buildings for various regions of Portugal was more successful. Unlike most studies for northern countries, it is predicted that the energy demand for space conditioning in Portugal would greatly increase by the end of the twenty-” rst century, assuming ” xed characteristics of the building sectors. The heating season is shorter and the heating thermal load reduced (with HadCM3 model scenarios, less 250–410 kWh for residences, less 5–7 kWh/m² for offices), but these economies are offset by an extended cooling season and large increases of the cooling thermal loads (additional 500–880 kWh for residences, 19–24 kWh/m² for offices). Higher resolution studies using HadRM data resulted in yet more serious climate change impacts.

Buildings and plant are designed utilizing near-extreme weather data. The present data used are brie‘ y discussed, including manual near-extreme percentiles for manual design and hourly data for simulation on a PC (test reference years and design summer years, and near-extreme periods). However, with climate change occurring, designs based on current data will produce uncomfortable summer thermal conditions within and around buildings in the future. This expected change is especially relevant now, as buildings have to last typically from 50 to 100 years. Climate change data for the future are needed to assess the performance of buildings and plant in the future. The Hadley Centre climate change models could provide such data. In this paper analysis of extreme data from one model, the HadCM3 model (south-east England grid box) with an appropriate climate change scenario, are considered in relation to their use for design assessment. Dry bulb temperature and solar irradiance extreme values are considered in this paper. The expected trend in both minimum and maximum temperature is for both to increase with time, but the maxima are found to rise faster than the minima. There are two factors in‘ uencing the solar radiation estimates, the basic clarity of the atmosphere and the seasonal amount of cloud. The latter is predicted to increase slightly in winter and decrease slightly in summer. The variations in the predicted short-wave radiation values re‘ ect the expected combined impacts of these two factors. The implications of these results are brie‘ y discussed.

Photovoltaic cladding on the surfaces of commercial buildings has the potential for considerable reductions in carbon emissions due to embedded renewable power generation displacing conventional power utilization. In this paper, a model is described for the optimization of photovoltaic cladding densities on commercial building surfaces. The model uses a modi” ed form of the ‘fill factor’ method for photovoltaic power supply coupled to new regression-based procedures for power demand estimation. An optimization is included based on a de” ned ‘mean index of satisfaction’ for matched power supply and demand (i.e., zero power exportation to the grid). The mean index of satisfaction directly translates to the reduction in carbon emission that might be expected over conventional power use. On clear days throughout the year, reductions of conventional power use of at least 60% can be achieved with an optimum cladding pattern targeted to lighting and small power load demands.

Most computer programs that analyse the yearly performance of buildings require an input of historical series of weather data measured at the location of interest. However, analyses of climate change scenarios needs hourly weather data series that allow realistic changes in various weather parameters. This can not be done easily with the reference years based on measured weather data normally used. In this paper a weather generator is proposed in which climate changes can be implemented. Based on weather data observed over years a stochastic weather model is developed. This model is used to make a general model independent of the location and with which climate changes can be simulated. The general model can be adapted to a local climate change. Only new averaged values of the weather variables being the result of a climate change should be used as input. The model is able to generate hourly weather data that can be used as input for all kind of simulation programs to study the effect of climate change on the design and energy consumption of HVAC systems.

While weather changes from day to day, climate change occurs on a time scale far in excess of a human lifetime. Climate had been changing dramatically even before humans evolved on Earth. Notwithstanding this, there is evidence supporting an accelerated global warming trend over the last century. Scientists agree that much of the global warming can be attributed to increases in certain greenhouse gases, notably water vapour and carbon dioxide. What scientists disagree over is what amount of the greenhouse effect is due to human activity. This paper's purpose is to quantify, through computer simulations, the increased cooling loads imposed by recognized climate change models and give designers a framework within which to set goals for new building efficiency measures. The paper does not include proposals for methods of reducing energy consumption in buildings – instead, it demonstrates the probable impact global warming trends have on buildings' cooling loads and vice versa.

Urbanization causes the so-called heat island phenomenon that raises urban atmospheric temperature higher than that in the suburbs. The phenomenon leads to a decrease in heating load and an increase in cooling load in the urban buildings. In this paper, the effect of the heat island phenomenon on the heat load of buildings was analysed by year-to-year changes in the heat load both in the urban and suburban areas. Tokyo was selected as the target urban area and Choshi, as a less urbanized city, among 30 stations located in a range of about 100 km both east-west and north-south centering on Tokyo. Investigation on the relationship between the changes in heat load during the past 100 years confirmed that

• The heating load of Choshi has decreased about 20% since 1900, whereas the heating load of Tokyo has decreased about 40% in the same period of time.

Also

• The cooling load of Tokyo has increased about 20% since 1900, but the similar increase can be seen in that of Choshi.

Building design has traditionally assumed an unchanging climate. Various weather statistics are used for design calculations. These numbers are by definition fixed in an unchanging climate, but most will change if climate change is occurring. This requires a re-examination of the weather design criteria for buildings. Regular updating may be sufficient, but has the disadvantage of always being retrospective, and other approaches may be needed. Severe weather events and global temperature patterns are well documented, but little work has been done to look at the trends in climate statistics for building design. This paper presents trends in some key weather statistics used in building design over the period 1977–1999. Finally, some conclusions are drawn on the use of climate statistics for building design when climate change is occurring.

Climate change will alter expectations about the future to such an extent that past statistics about averages, variability and extremes will no longer be relevant. Scenarios of the future depend upon the rate of greenhouse gas increases (and changes in aerosol distributions) in the atmosphere, and are best produced by coupled ocean-atmosphere climate models. Uncertainties in the scenarios are large, however, not only because future emissions have to be estimated, but also because the climate system is complex and varies considerably from decade to decade naturally. The temporal and spatial scales for which information can be produced generally means that detail can only be developed through downscaling procedures. Until recently, downscaling was generally achieved by statistical means using relationships between spatial scales. Increasingly, downscaling is now being undertaken using regional-scale models embedded within the global model (e.g., for the scale of Europe). Resolutions of 50 km in space at the daily timescale are possible, but just as with the large models there is still a need for model verification, particularly with respect to the daily variability and extremes, the timescale at which much of climate change is perceived by nonatmospheric scientists.