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Tabular Data - 8.8 KB - 5 Variables, 133 Observations - UNF:6:fOBEAatqM2rpQmxIr+BMGg==
Tabular Data - 8.6 KB - 5 Variables, 133 Observations - UNF:6:halccI+5GXJqSbWrBSL5Vg==
Tabular Data - 8.2 KB - 5 Variables, 133 Observations - UNF:6:y2GzcoyKSGX0PiaFz48M0A==
Tabular Data - 7.9 KB - 5 Variables, 133 Observations - UNF:6:fg8V8RSnjifRuyVOcW6hwQ==
Tabular Data - 8.5 KB - 5 Variables, 133 Observations - UNF:6:2O02c90orUW1owV0D+Vq7Q==
Nov 17, 2025
Kishore, Aparna, 2025, "REVI-Twin: An Integrated AI-Driven Methodology for Creating Digital Twin of Residential Electric Vehicle Infrastructure", https://doi.org/10.18130/V3/IWOVOX, University of Virginia Dataverse, V1, UNF:6:18f6sfDQqMlTGN8qnwaGLg== [fileUNF]
Integrating electric vehicles (EVs) into homes and the electrical grid introduces complex dynamics that can overwhelm planning tools. Accurately estimating hourly residential EV charging demand typically requires computationally intensive agent-based simulations that depend on large volumes of input data. Such requirements limit scalability of the...
Tabular Data - 39.8 MB - 75 Variables, 36712 Observations - UNF:6:Mpk1nyJR5Nj4XVQwJWyT4g==
Tabular Data - 39.7 MB - 75 Variables, 36712 Observations - UNF:6:66usOgI1I1KDFTuKTLbxCA==
Tabular Data - 312.3 MB - 49 Variables, 361896 Observations - UNF:6:poO5xmA38fDVUol6fpzdWA==
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