Through NASA's DEVELOP National Program, we worked with the Langley Research Center (LaRC) and partnered with Washington DC's Department of Energy and Environment (DOEE) to create solar potential maps to inform solar panel installation decisions for the District.
An accepted paper at the NeurIPS CCAI Tackling Climate Change with Machine Learning 2021 Workshop. We modified existing deep learning-based super-resolution models, and applied them to satellite data to increase the resolution of wind speeds and solar irradiance fields for informing short-term, local energy planning. We published machinelearning-ready wind and solar datasets.
I built upon Netlab's Adaptive Charging Network (framework for electric vehicle charging) to improve accuracy of power-consumption predictions and optimize charging for an individual user. We used optimization theory to parse through battery charging data and schedule energy loads in a vehicle fleet.