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The Power of Reflection: Predicting the Value of Solar Thermal Systems
By Chris Laughton, author of Solar Domestic Water Heating.
In my book Solar Domestic Water Heating I highlighted the complexity of accurately predicting the performance of a solar assisted heating system. Whilst there are some useful rules-of-thumb for calculations, there are also many caveats involved in using them. Indeed, all simple calculation methods that are published for solar energy systems have notable margins of error that must also be considered.
To tighten up our prediction accuracy we would need to consider how to better measure-up prospective sites, and in particular how we assess them for annual solar radiation levels. The first step in this process is to begin with a reliable prediction of the raw solar radiation that is likely to fall to earth based on historical meteorological records. We then make mathematical allowances for the collector’s orientation. Whilst these characteristics are important, the aspect that site assessors can most readily improve is their shading assessments.
For example, one oftentimes-overlooked factor in shade analysis is the reflectance of solar radiation. Yet the simple act of reaching for our sunglasses to shade our eyes when looking at brightly-colored objects shows how our own bodies already understand the magnitude of reflectance. The average of reflected radiation of the earth as seen from space, the albedo, is 0.2. In other words, 20% of incident radiation is reflected. Indeed, it is only because light is reflected that we perceive it to have a color at all. Most performance figures assume the reflectance value as being present for inclined collectors, which increasingly benefit from reflected radiation as their pitch angle is steepened. However, it is where there is a strong difference in color between the average green/brown/blue spectrum assumed in the average albedo that the predictions start to go awry. This can be by as much 20% in cases such as where a large brightly-colored building is placed in front of a façade-mounted collector. Snow can also temporarily increase reflectance to similar values, although oftentimes the snow also covers the solar thermal collector entirely and so cancels such gains out. The reverse effect occurs where solar installations are situated near black surfaces such as bituminous tarmac that tend to reflect lower than the average albedo. For most installations, the effects of reflectance will only cause a small deviation from the assumed average value; but if we want to tighten the margins then this is an area we should not overlook.
Shading at first seems more clear-cut. Most people assume that if there is a percentage loss of sky above the horizon then this equates to a loss of solar radiation arriving at the collector. However, the quantity of radiation from each part of the sky varies with its compass direction (azimuth) and its height. To put it another way, the sunpath varies with the time of day and seasons and so different parts of the sky are valued differently. In general, the lowest parts of the sky give the weakest radiation contribution. These areas are conversely at the highest risk of shading from distant landscapes objects. Nevertheless, the effect is cumulative; and what is not always appreciated is that at every point in the UK mainland at least 2% of the sky is already obscured by the landmass that sits above sea-level. This reduction can be far greater than this minimum percentage due to additional landscape features such as hills and buildings above the horizon. It is shading objects near to an array that have the greatest effect, as these are most likely to cause hard shadows by blocking out the direct (beam) solar radiation.
It is no longer considered sufficient to record a simple silhouette outline of distant objects as the width and distance of nearby objects is also important. Objects can cause hard shadows even from over 100 metres away. The industry has now developed electronic and digital methods to capture such information quickly transferring the data into computer simulation software to accurately predict performance.