Predicting Electric Vehicle Charging Station Usage from Historical Data

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It is increasingly important that utilities have a precise view of how much energy their grid will need to provide to support the rapid growth of electric vehicles and the charging infrastructure. Meanwhile, electric vehicle users want to know where best to charge their vehicles.Mogile Technologies operates ChargeHub, a platform that, amongst other things, helps consumers find charging stations on the go. In support of this product, they aggregate data on charging station usage; if they can use this data to predict usage, it will dramatically improve and expand the services they can provide to both utilities and consumers alike.The objective of this project is to identify machine learning models that best predict future charging station usage. We will explore a variety of approaches, from regression to deep learning; we will work with a variety of input features, including those the characterize past charging events (e.g., per charge energy), and, time permitting, additional variables expected improve accuracy (e.g., weather conditions). As domain experts, Mogile will work with us closely to identify the appropriate strategy for constructing a set of training data. As machine learning experts, we will build and evaluate models that, given (a) training data, and (b) past usage, (c) predicts usage over the next seven days, and (d) associated prediction confidence. This work will delivered in the form of a prototype API, and documented in the form of a deliverable report.