Unsupervised Temporal Clustering to Monitor the Performance of Alternative Fueling Infrastructure
This work addresses the need for reliable infrastructure to support zero-emission vehicle adoption, but it is incremental as it applies existing clustering techniques to a new domain.
The researchers tackled the problem of monitoring alternative fueling infrastructure reliability by developing an unsupervised temporal clustering approach combined with survey analysis, applied to hydrogen stations in California, demonstrating a method to assess performance without specifying concrete numerical results.
Zero Emission Vehicles (ZEV) play an important role in the decarbonization of the transportation sector. For a wider adoption of ZEVs, providing a reliable infrastructure is critical. We present a machine learning approach that uses unsupervised temporal clustering algorithm along with survey analysis to determine infrastructure performance and reliability of alternative fuels. We illustrate this approach for the hydrogen fueling stations in California, but this can be generalized for other regions and fuels.