Washington has recently passed two pieces of legislation that impact electric companies in the state: the Clean Energy Transformation Act (CETA) and the Zero Emissions Vehicles Law. They require that the state’s electricity supply be free of greenhouse gas emissions by 2045 and for all new vehicles sold in the state to be zero-emission vehicles by 2035, respectively. In addition to moving away from coal-fired power plants, CETA states that utilities must consider the equity impact of these clean energy investments on vulnerable populations and highly impacted communities. To address this, utilities are developing energy efficiency incentives and community-based distributed energy resources. The changing nature of our state’s electric usage patterns due to these investments in community programs and an increase in electric vehicle (EV) adoption will put new stresses and strains on our electrical grid that we need to understand. In this talk I will focus on the model we developed at a Washington utility to detect EVs in order to understand their impact on our electrical grid.
In the coming decades, most electric vehicles are expected to be charged at single-family residences in the evening hours, rather than public charging stations at all hours. In order to prepare for the increased load on our power grid during peak times, electric companies need to know when and where the charging is happening. Building an EV charging detection model is difficult because the expected population of EVs is around 1-5%; we have a highly imbalanced problem. Starting with a relatively small labeled dataset, we built an EV detection model using novel step-detection features in time-series data. Using a random forest classifier, we are able to achieve accuracy, precision, and recall metrics of over 80%. In order to validate our data with what we expect among our population of customers, we compared our results to aggregated data from the Department of Licensing as well as survey results from our customers.
Overall, our stakeholders are satisfied with our model and its ability to predict which customers are charging an EV. I will discuss how we work with our stakeholders to understand which metrics we need to optimize for in order to help them prioritize their maintenance work. I will also briefly discuss next steps for this model.
Andrea Urban is an astronomer-turned-data scientist. When she isn't chasing the next total solar eclipse, she enjoys looking for patterns in data and building bespoke machine learning models.
