Laetitia Uwineza | Universidad del Rosario (original) (raw)
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Papers by Laetitia Uwineza
Journal of the Korean Solar Energy Society
The typical meteorological year (TMY) is a key element in the long-term performance predictions o... more The typical meteorological year (TMY) is a key element in the long-term performance predictions of photovoltaic systems. However, there is a loss of some hourly measured weather data during the formation of the TMY dataset owing to a lack of observations. Therefore, various statistical techniques or satellite observations can be utilized to address this limitation, and these interpolated data may represent some degree of uncertainty that can influence the accuracy of the TMY data. This may in turn affect the long-term planning reliability of the photovoltaic systems. Therefore, a bootstrap method was used in this study to evaluate the accuracy of TMY datasets in the prediction of long-term photovoltaic yields. The electricity production uncertainties obtained through a simulation conducted using this TMY were calculated with a confidence level of 95% for each resulting value as validated based on the long-term average electricity yield. The results show that the bootstrap method provides valid and more useful information than the deterministic approach. Therefore, it is the best method for quantitatively analyzing uncertainty in TMY datasets. The results described herein can serve as a guide for risk management strategies and other business decisions related to solar energy projects. In addition, the results can help planners achieve greater confidence when applying TMY data to feasibility studies.
Journal of the Korean Solar Energy Society
The typical meteorological year (TMY) is a key element in the long-term performance predictions o... more The typical meteorological year (TMY) is a key element in the long-term performance predictions of photovoltaic systems. However, there is a loss of some hourly measured weather data during the formation of the TMY dataset owing to a lack of observations. Therefore, various statistical techniques or satellite observations can be utilized to address this limitation, and these interpolated data may represent some degree of uncertainty that can influence the accuracy of the TMY data. This may in turn affect the long-term planning reliability of the photovoltaic systems. Therefore, a bootstrap method was used in this study to evaluate the accuracy of TMY datasets in the prediction of long-term photovoltaic yields. The electricity production uncertainties obtained through a simulation conducted using this TMY were calculated with a confidence level of 95% for each resulting value as validated based on the long-term average electricity yield. The results show that the bootstrap method provides valid and more useful information than the deterministic approach. Therefore, it is the best method for quantitatively analyzing uncertainty in TMY datasets. The results described herein can serve as a guide for risk management strategies and other business decisions related to solar energy projects. In addition, the results can help planners achieve greater confidence when applying TMY data to feasibility studies.