Difference between revisions of "Self-Contained Solar Energy Experiment Kits"

Revision as of 19:03, 8 January 2016

Mentors: Lisa Bosman and Dennis Brylow.

Title: Development of Self-Contained Solar Energy Experiment Kits

Motivation: Many solar energy predictor calculators use historical weather and solar irradiance data to estimate future solar energy system performance, and thus, valuation. Practically speaking, the accuracy of these estimations is important to many industry stakeholders. First, for the insurance industry, accurate estimation of solar panel valuation is important if solar panels are damaged by hail or wind, because home owners can file an insurance claim based on the current and/or remaining value. Second, for the realty industry, when someone is trying to sell their home and plan to leave the solar panels on the roof, the added value of the solar panels will affect the home selling price. Third, for government purposes, the value contributed by solar panels will determine the property tax assessment. Fourth, for consumers (homeowners, businesses, and utility companies) who are purchasing and installing the solar panels, accurately estimating the solar panel valuation will ensure an accurate and timely return on their investment. This is a challenging project that is well-suited for students with an interest in programming, statistical analysis, and renewable energy; students will learn and apply valuable skills and tools widely used in industry. Objective 1: Develop a solar energy sensor to collect and store weather and solar irradiance data including solar panel temperature and incoming solar irradiance.

Objective 2: Develop a graphical user interface for entering solar panel specific data including DC power rating and temperature coefficient, and incorporating with the stored weather and solar irradiance data for the purpose of predicting the availability of AC power.

Objective 3: Develop self-contained solar energy experiment kits for middle and high school teachers to educate students on statistical analysis concepts through the perspective of renewable energy.