Quantifying the effects of averaging and sampling rates on PV system and weather data

When modeling photovoltaic (PV) system performance data, modelers typically reduce the amount of data analyzed by reducing the sampling frequency below the maximum sampling frequency of their instruments (under-sampling), averaging a number of samples together, or a combination of these two methods. A sampling frequency which is too low may not provide enough fidelity to accurately model system performance, while a sampling frequency which is too high may provide unnecessarily high data fidelity and increase file size and processing complexity. This paper strives to quantify the errors caused by reduced sampling and averaging frequencies through the comparison of modeled high temporal resolution weather data and low resolution weather data.