Recent advances in renewable energy and its increased adoption, especially at Caltech, are adding pressure to the distribution grid and threatening its power quality. In order to prepare the power grid for fluctuations and increasing load, we must coordinate and optimize the nodes in the power system to form a smart grid. In order to visualize and interpret the results of network calculations, we are building a software interface that provides information about the state of the grid. This interface is a dashboard that queries live data from meters around campus and displays metrics such as kilowatt usage, meter outages, and power imbalance. It can be immediately applied to identify under-metered areas and buildings with power factor issues, which can damage equipment and incurs penalty fees. Finally, we are applying machine learning methods to determine correlations between different electrical readings and identify focus areas with the greatest impact on the overall power factor. This will help Caltech achieve long-term goals, such as replacing power from the gas plant with renewable resources and reaching net zero emissions.