Inspiration

Our project was inspired by a fundamental realization that universities don’t necessarily have an energy shortage they have an energy management problem. While analyzing the case, we noticed that energy usage was not just high, but poorly distributed across time and building types.

The concept of time based pricing and peak demand costs stood out. It became clear that shifting when energy is used could be just as impactful as reducing how much energy is used. This led us to focus on optimizing HVAC systems and campus wide energy utilization to better align with pricing and demand cycles.

What it does

Our Project optimizes campus energy usage by improving HVAC control and shifting energy demand to lower cost, off peak periods. By focusing on high impact buildings like research labs and aligning usage with time based pricing, it reduces costs and emissions without disrupting operations.

How we built it

We built our solution using a structured engineering and financial approach, starting with system decomposition and root cause analysis. We identified key inefficiencies, modeled energy cost as a function of time, and designed a phased strategy combining smart metering, predictive analytics, and targeted HVAC optimization.

Challenges we ran into

One of the biggest challenges was working with limited data, as most buildings lacked real time monitoring. We also had to balance ambitious ideas with strict constraints like budget limits, no downtime for labs, and the need for measurable results within 3 years.

Accomplishments that we're proud of

We’re proud of developing a solution that is both technically grounded and financially justified. Our approach prioritizes high impact areas, clearly explains tradeoffs, and presents a realistic implementation plan that could be deployed in a real university setting.

What we learned

We learned the importance of focusing on root causes rather than symptoms and how to think in terms of systems rather than isolated fixes. We also gained experience in evaluating tradeoffs and using financial metrics like ROI and payback period to guide engineering decisions.

What's next for Boiler Optimization System Specialist

Next, we would refine our predictive models using real time data and expand our solution to include additional systems like lighting and equipment scheduling. We would also look to pilot the system in a subset of buildings to validate performance before scaling campus wide