Women in Data Science Puget Sound 2024

With the accelerating global transition towards sustainable energy, the demand for Electric Vehicles (EVs) has surged, necessitating advancements in EV charging infrastructure. Please join me for an exciting tour of leveraging deep learning in electric vehicle (EV) charging demand prediction! I will present an application we developed, utilizing deep learning to predict EV charging demand and corresponding energy savings, a crucial aspect in optimizing energy distribution and promoting sustainable transportation. Our application explores various deep learning models, including multiple linear perceptrons (MLP), convolutional neural networks (CNNs), long short term memory (LSTM), and a transformer, to analyze historical EV charging data alongside external variables influencing charging behaviors. I’ll present the results from different deep learning models and how to turn them into practical solutions. I will also present some fun visualizations of our results! Our application is open to all and takes advantage of publicly available data. In summary, it can serve as a tool for policymakers and/or urban planners in anticipating peak usage periods, optimizing resource allocation, and minimizing strain on the power grid!
This paper is coauthored with Mayuree Binjolkar.