Budget-Aware Rooftop Solar Allocation in Holyoke

Project Description:  

 Students will use building-level and rooftop-solar data from Holyoke, Massachusetts to study how a resource-constrained town could support solar installation. They will estimate each eligible commercial or apartment building’s installation cost and expected annual electricity generation from available roof area and sunlight. First, students will identify the maximum achievable solar benefit under a fixed subsidy budget. They will then model a sequential version of the problem, in which business owners request incentives over time and the town must make accept-or-reject decisions without knowing future requests. The project introduces empirical research through a practical optimization problem at the intersection of sustainability, public policy, and computing.

Learning Objectives:  

1. Formulate a real-world question as a measurable, data-driven research problem.

2. Clean, explore, and interpret a geospatial building-level dataset.

3. Estimate solar-system cost and energy production using transparent modeling assumptions.

4. Compare optimization strategies for allocating a limited public budget.

5. Understand the distinction between offline decision-making and online decision-making under uncertainty.

Skills needed:  

• Python-based data analysis using Pandas, NumPy, SciPy etc.

• Basic optimization modeling