Resource Optimization: Minimizing the Cost of Solar Panel Installation


  • Jeremy Suh Aspiring Scientists' Summer Internship Program, 2019
  • Carmen Caiseda Department of Mathematical Sciences, College of Science, George Mason University
  • Dr. Padmanabhan Seshaiyer Department of Mathematical Sciences, College of Science, George Mason University



Currently, solar power accounts for 1.6% of all United States electricity generation. In the midst of many movements to try to reduce our carbon footprint, going solar is highly incentivized. In addition, solar panels are becoming exponentially more efficient, making solar a more viable option. This mathematical modeling project utilizes an optimization method, linear programming, in order to provide users with the lowest possible cost of solar power installation. The basis of linear programming surrounds optimizing an objective function (cost) subject to linear equality and inequality constraints. These constraints surround a user’s needed kilo wattage and household available space for solar panels. Currently, companies have solar panel calculators that account for user data but they all assign plans based on predetermined panels. This yields an expensive solar panel plan that discourages the consumer. This model is highly adaptable and has the ability to be applied to any household globally. It also explores different methods for more specific solar power calculations using predictive models based on publicly available data. In fact, this project shows that anybody planning to live in their house for over 14 years will profit from the implementation of solar panels. The overall purpose of this project is to create an interface that instantly calculates a consumer’s cheapest (optimal) possible solar power plan to promote green energy. Going forward, we hope to develop more accurate techniques so that solar companies can build effective technologies that are both energy and cost-efficient.






Abstracts from the 2019 Aspiring Scientists' Summer Internship Program