Investigating EV charging data

Introduction:

Got an opportunity to work with a home monitoring technology company over the last 6 months whose goal was to help people understand their household energy consumption, know what is going on in their homes, and ultimately reduce their energy footprint.

With the company business goal in mind formulated the following Problem statement:- Help equip customers with insights about their EVs energy consumption and help predict future charging behavior.

Dataset provided consisted of 1200 Households across the USA with one or more EVs of various brands at each Household. For each EV hourly energy consumption was provided. The dataset lacked consistency and was not uniformly populated for each EV during the time range. Some EVs had data for 6 months, while some EVs had data for just 3-4 hours.

The inconsistency started by deep diving into the dataset with two EDAs to understand the dataset better and explore factors that could be impacting the energy consumption of an individual car.

Some interesting insights from the EDA performed:

EDA Part 1: https://public.flourish.studio/story/1113717/

EDA Part 2: https://public.flourish.studio/story/1113715/

Please refer to EV Consumption PDF attached to see the EDA summary and Modeling Approach.

Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior. Created energy consumption-wise clusters and LSTM model for future consumption insights. Designed sample dashboard views with insights and recommendations for customers.