研究目的
To calculate cubic splines that define the behavior of global solar radiation in a high Andean equatorial location, using functional data analysis and statistical methods to study its variations and detect outliers.
研究成果
The functional treatment of daily global solar radiation data using cubic splines and bootstrap resampling effectively characterized its behavior, identifying two distinct patterns in the monthly averages that did not align with the defined climatic periods. This approach provides a detailed visual and statistical understanding of solar radiation variations in equatorial Andean regions.
研究不足
The study is limited to data from a single year (2010) and a specific location, which may not capture long-term trends or be generalizable to other regions. The equipment has a typical error of 5%, and the bootstrap method relies on assumptions of normality and stationarity.
1：Experimental Design and Method Selection:
The study uses cubic spline interpolation in a normed orthogonal functional space to transform discretized data into continuous functions. Bootstrap resampling is employed for outlier detection and averaging.
2：Sample Selection and Data Sources:
Data from 2010 on global solar radiation measured every 10 minutes at a meteorological station at 2480 m above sea level in an equatorial Andean region, forming a matrix of 52560 data points.
3：List of Experimental Equipment and Materials:
A Li-Co #LI-200SA pyranometer with a 5% typical error and an NRG Symphonie data logger.
4：Experimental Procedures and Operational Workflow:
Data was collected and converted to .txt format using Symphonie Data Retriever, then analyzed in R software version
5：2 with the fda.usc library to compute functional means, detect outliers using bootstrap with 1000 resamples, and calculate cubic splines. Data Analysis Methods:
Functional data analysis, including computation of norms, means, and confidence bands using L2 metrics and bootstrap methods.
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