Solar power integration into the grid is important for combating climate change, but variability in solar irradiance poses a challenge. We introduce CrossViViT, a deep learning model that utilizes satellite data to accurately forecast Global Horizontal Irradiance (GHI) for the next day. Our model also provides prediction intervals for each time-step, indicating the level of forecasting uncertainty. CrossViViT performs well in solar irradiance forecasting, even in unobserved solar stations.

We present a collaboration with the OCP Group, one of the world's largest producers of phosphate, in support of a green initiative to reduce OCP's carbon emissions significantly. We study the problem of decarbonizing OCP’s electricity supply by installing a mixture of solar panels and batteries to minimize its investment cost plus the cost of satisfying its remaining demand via the Moroccan national grid. This forms the basis for a one billion USD investment in renewable energy generation.

In rural areas of Sub-Saharan Africa, people’s main source of clean drinking water comes from handpumps. However, across SSA, one in four handpumps are broken at any given moment. In this project, we study the maintenance program of three NGOs. We develop a MDP model to study the optimal schedule of mechanics visiting water points under different information availability and logistics structure. The goal is to minimize water point downtime.