This addresses two separate issues that the human mind often confuses. The first is that not all PV module degradation data is fully and accurately interpreted. Second is the assumption that full and accurate data interpretation will necessarily lead to deeper insight and more accurate future predictions. It is, however, not clear how much additional information can be gained from secondary signals (like luminescence) when they are “optimized.” Often, these issues are reflected in data analyses that deal only with linear approximations., ignoring data that do not fit linear patterns. When claims are made that such data isn’t public or does not exist with great enough accuracy, this may or may not reflect the real situation for making more accurate predictions. While it may be instructive to understand PV losses in terms of per year (annual) loss, it has to be realized that failure and degradation may be different if multiple degradation mechanisms are effective. A confusion may exists between average and individual numbers and greater accuracy and more data will not automatically guaranty deeper insight into an issue or allow more accurate future projections for an individual case. The issue is whether or not more insight is gained by separation to identify the largest mechanism, or whether the correct combination of features minimizes degradation mechanisms. The results of the author’s personal home PV system (in its 7th year) are presented.
KEYWORDS: Solar cells, Data modeling, Systems modeling, Solar energy, Photovoltaics, Performance modeling, Renewable energy, Solar radiation models, Beryllium, Statistical modeling
This paper poses the question how statistically varying data should be handled while the output of modeled
real-world systems is sometimes interpreted by human preference? It asks the question whether modeled data or realworld
data are of greater importance. This question is asked in the context of what is more important, having a model
that correctly predicts real-world energy generation or cost of a PV array or the "typical" generation or cost for such
array? Real-world performance can only be predicted within some uncertainty level. Are good average or individual
values obtained when the real-world energy output or cost of a single system are predicted? Two or more input factors
into a single output will also often lead to increased variation. The point is made that greater accuracy of modeled and
experimental data may or may not result in deeper insight into the generation capability of a solar array. The answer to
the question posed in the title of this contribution is: Be as accurate as possible, but always expect variation in both the
calculated and experimental results and be cognizant of the difference between typical and actual values.
This paper discusses the critical strategic research and development issues in the development of next-generation photovoltaic technologies, emphasizing thin-film technologies that are believed to ultimately lead to lower production costs. The critical research and development issues for each technology are identified. An attempt is made to identify the strengths and weaknesses of the different technologies, and to identify opportunities for fundamental research activities suited to advance the introduction of improved photovoltaic modules.
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