Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego

Abstract

Solar irradiance forecast accuracy of a ground-based sky imaging system currently being developed at UC San Diego is analyzed by assessing its performance on thirty-one consecutive days of historical data collected during winter. Sky images were taken every 30s, and then processed to determine cloud cover, optical depth (thick or thin), and mean cloud field velocity. Cloud locations were forecasted using a frozen cloud advection method at 30s intervals up to a forecast horizon of 15min. During the analysis period, cloud field matching errors, which monotonically increase as a function of forecast horizon, did not exceed 30% over the sky imager’s field-of-view. On average, frozen cloud advection forecasts were found to perform superiorly to image persistence forecasts for all forecast horizons during the analysis period.Six (later eleven) distributed pyranometer installations over the UCSD campus provided 1-s instantaneous GHI measurements with which to validate irradiance forecasts. Excluding clear days or days with small forecast sample size, sky imager irradiance forecasts were found to perform the same as or better than clear sky index (clear-sky normalized GHI) persistence forecasts on 4 out of 24days for 5-min forecasts, 8 out of 23days for 10-min forecasts, and 11 out of 23days for 15-min forecasts. Furthermore, visual comparison of forecast irradiance with measured irradiance revealed the ability to accurately predict cloud-induced irradiance fluctuations, which persistence forecasts cannot offer.An additional month of data collected during summer was analyzed to evaluate performance consistency during a time period with different meteorological conditions. Due to sky conditions favoring persistence forecast and challenges with cloud detection, sky imager forecasts were unable to surpass persistence forecasts for all 32days for 5-min forecasts and only succeeded on 1day for 10-min forecasts. However, bulk errors indicated consistency with winter forecasts, with rRMSE of 24.3% (20.0% for winter) and 27.7% (22.9%) for 5- and 10-min forecasts, respectively.A discussion of the challenges and sources of error applicable to the sky imaging system used is also presented, as well as future research intended to address potential areas of improvement.