Research, Issues, and Recommendations for a New Solar Energy Prediction Model

Undergraduate #121
Discipline: Technology and Engineering
Subcategory: Climate Change

Lincoln Peters - College of Menominee Nation
Co-Author(s): Travis Spice and Lloyd Frieson, College of Menominee Nation, Keshena, WI

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The advent of solar energy can potentially improve the costs of energy consumption on global, national, and tribal levels. Interest in this abundant, renewable power source spans from governmental entities, to utility companies, corporations, and residential homeowners. In each case the question of if and when the investor will recoup their initial investment is an important barrier to be considered. The Internet is ripe with products that will calculate the costs and benefits of investing in solar energy, but what if the people making data driven decisions about solar energy were working with incomplete models? In this research, we hypothesize the need for a better model when estimating the performance and valuation of roofmounted solar energy systems.

This research utilizes data from two different solar energy data sources, including the College of Menominee Nation’s Solar Energy Research Institute (Keshena, WI) and Argonne National Laboratory’s Midwest Photovoltaic Analysis Facility. Performance comparisons are made from 5 different types of solar energy technology, including Monocrystalline Silicon, Polycrystalline Silicon, Amorphous Silicon, Cadmium Telluride, and Copper Indium Gallium Selenide, and two different types of inverter systems, including micro-inverter and string inverters. Both sites include weather and climate data, such as incoming solar irradiation, temperature, and wind speed.

Three major inaccuracies were found when comparing the actual solar panel data to current, free, online models readily available. First, it was found that one type of silicon solar energy technology outperformed another type of silicon solar energy technology. Second, the micro inverter outperformed the central inverter. Third, there is variance in seasonal impacts in estimating the actual versus theoretical prediction based on temperature and solar irradiance.

In conclusion, this exploratory research provides motivation for the development of a new solar energy prediction model, with respect to technology selection, inverter selection, and seasonal derate options. A more accurate model will aid manufacturers, consumers, contractors, and government with decision making relative to the performance and return on investment for solar energy systems.

Funder Acknowledgement(s): NSF TCUP CORE

Faculty Advisor: Lisa Bosman,