Electric Power Systems Research · 2020
Classification of electric vehicle charging time series with selective clustering
Electric Power Systems Research
What battery behavior can be learned from imperfect charging records?
An iterative procedure extracts and clusters the tail portions of charging curves despite missing observations, variable lengths, scheduling effects, and measurement noise. Experiments on ACN data illustrate how these patterns can support useful charging models.
Research themes
Connections
Recovering structure from limited measurements
Graph recovery estimates electrical network parameters. Charging curve clustering and energy disaggregation recover useful structure from observed behavior and aggregate meter readings.
Cite this paper
Chenxi Sun, Tongxin Li, Steven H. Low, Victor O. K. Li. Classification of electric vehicle charging time series with selective clustering. Electric Power Systems Research, 2020. https://doi.org/10.1016/j.epsr.2020.106695
BibTeX
@article{tongxin-charging-clusters,
title = {{Classification of electric vehicle charging time series with selective clustering}},
author = {Chenxi Sun and Tongxin Li and Steven H. Low and Victor O. K. Li},
year = {2020},
journal = {Electric Power Systems Research},
url = {https://doi.org/10.1016/j.epsr.2020.106695},
doi = {10.1016/j.epsr.2020.106695}
}
Related papers
Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning
DualNILM jointly recognizes appliance states and identifies energy injected behind the meter. Its transformer architecture combines temporal learning tasks to separate consumption from injections, with evaluation on measured and synthesized datasets.
Can appliances be identified when solar and storage obscure meter readings? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometryLearning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications
Bounds for noisy and noiseless graph recovery connect sparsity, graph distributions, and linear measurements. A three stage recovery scheme and practical algorithm are studied on canonical graphs and electrical grid admittance matrices.
What determines the sample complexity of network reconstruction? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization