Knowledge-Based Systematic Feature Extraction for Identifying Households With Plug-in Electric Vehicles

Abstract

Hassle-free in-house charging of plug-in electric vehicles (PEVs) is getting popular. Due to high power consumption, in-house PEV charging has a significant impact on the distribution network, and the utility companies are facing challenges in balancing the power demand. The problem could be solved by increasing distribution network capacity, designing an effective demand response program aimed at PEV charging, implementing vehicle to grid (V2G) or vehicle to house (V2H), and so on. One of the fundamental parameters for the mentioned solutions is to identify households with PEVs. Multi-level charging, power consumption similar to other home appliances, and absence of submeter for charging outlets make identification difficult. In this paper, a new feature extraction technique called knowledge-based systematic feature extraction is proposed to identify households with PEVs. The extracted features are easily interpretable and validated using two datasets from different regions. Keeping real scenarios in mind, the study examines scenario-based results and finds that the accuracy using extracted features ranges from 80.20% to 100% depending on classifier, number of vehicles, and level of charging. Moreover, results show improved performance compared to existing methods for identifying households with PEVs and other state-of-the-art feature extraction techniques.