Principal Investigator for MPC/RL Frameworks Evaluation

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We are looking for an entrepreneurial, analytical and passionate collaborator to work with us on an NSF SBIR proposal to advance the affordability of clean building control for low income and underserved building owners using AI. Our concept paper was encouraged and we hope to submit a full proposal prior to the March 2 deadline. We'd prefer a collaborator who is interested in potentially joining the CEL team either prior to or after the first phase of the work is complete - someone who wants to be part of building an impactful technology and team from the ground up with us. This would be your and our chance to evaluate that possibility.


We are currently partnered with more than 10 K12 school districts throughout the US to collect building energy modeling parameters as well as to instrument their facilities for the collection of real world operational energy data. Within the controls module of our software we are currently testing various energy management approaches in an attempt to understand which pathway(s) are the most commercially viable.

Initially we are primarily concerned with the optimization of building HVAC for responsiveness to grid signals, pricing and carbon while minimizing impact on IAQ and thermal comfort. Our goal is to reduce the installed and operational cost of smart controls for a variety of use cases within the target market segment of K12 schools.

We would like an expert to help us evaluate several ML frameworks given real world use cases and data. We hope to determine which frameworks have the most potential to be replicated—without significant customization —in other similar buildings and to verify and optimize that most promising model architectures using real-world data and use cases.

Please feel free to get in touch with our CEO, Tanya Barham, directly via LinkedIN.

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