Forecasting Electric Generation from Solar Energy
Some of the lessons learned include:
* Addressing the efficiency of electric generation as a function of irradiance and other variables
* Site data reveals key differences from manufacturer specs. Site data are key to making a good power forecast
* Sun-tracking systems may malfunction, affecting the angle of the solar panels and thus the energy available to them and resulting power output. The actual angles can be reliably calculated under clear sky conditions or over several days when there are enough sampled clear-sky times
* Site data requires quality control, including removing erroneous readings that are within a physically plausible range (such as when the instrument or recorder gets stuck for a short period)
* Care must be taken to not include times of accumulating snow in datasets used for training or developing algorithms
As we develop further experience before the conference, we will learn more lessons which can help raise the bar on solar energy forecasting. Additionally, MDA is participating in the large NCAR-led DOE-supported solar forecasting initiative and we may have other developments stemming from that collaboration to report on at the conference.