A Smarter Typical Meteorological Dataset: Advancing Accuracy in Solar Energy Yield Estimation

 As solar energy deployment accelerates, accurate resource data is essential for effective project development and minimizing financing risks. Traditional models use typical meteorological year (TMY) datasets, which are based on horizontal irradiance and may not accurately represent generation from PV systems at various orientations. This poster introduces an enhanced method using typical plane of array year (TPY) datasets, which account for the specific geometry of solar panels (fixed-tilt and single-axis tracking). Analyzing 21 years of National Solar Radiation Database (NSRDB) data, TPYs demonstrate significantly reduced energy yield prediction errors-within ±1%-2% annually versus ±5% or higher with TMYs, and up to ±50% in extreme cases. This indicates TMY modeling can introduce considerable yield bias, affecting financial outcomes.TPY-based modeling presents a more accurate, geometry-specific approach for solar energy predictions.This poster presents these findings.