The Sunairio Method for Solar Production Risk Assessment
A CLIMATE-CHANGE-AWARE APPROACH
Solar production risk assessment is traditionally based on hypothetical power generation on Typical Meteorological Years (TMYs) or historical weather data. These approaches ignore climate trends and extrapolate from small datasets, leading to production estimates that are unreliable and drift from reality over time. Sunairio addresses these shortcomings via a system of stochastic climate simulations to create arbitrary amounts of realistic local- climate-adjusted weather data.
On 100 representative US solar sites, we first verified that the Sunairio simulation method generated realistic weather. We then found TMYs to be unrepresentative and to overpredict power generation at 85% of the sites. We found site-dependent local-climate-trend production adjustments to range from -6.16% to 2.77% of predicted production in 2022 — adjustments that grew in magnitude to -13.48% to 4.63% when extrapolated out to 2034. An overall negative GHI trend of −0.225 W/m² per year (83% of the sites had negative GHI trends) caused overall production losses of 2.43% in 2022 and 4.98% in 2034 with respect to TMY estimates. Finally, the degree of uncertainty in production estimates was at least four times lower in the Sunairio simulation data compared to production estimates using historical samples.
On 100 representative US solar sites, we first verified that the Sunairio simulation method generated realistic weather. We then found TMYs to be unrepresentative and to overpredict power generation at 85% of the sites. We found site-dependent local-climate-trend production adjustments to range from -6.16% to 2.77% of predicted production in 2022 — adjustments that grew in magnitude to -13.48% to 4.63% when extrapolated out to 2034. An overall negative GHI trend of −0.225 W/m² per year (83% of the sites had negative GHI trends) caused overall production losses of 2.43% in 2022 and 4.98% in 2034 with respect to TMY estimates. Finally, the degree of uncertainty in production estimates was at least four times lower in the Sunairio simulation data compared to production estimates using historical samples.
