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Impact of uncoordinated plug-in electric vehicle charging on residential power demand
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“In comparison, deep learning models like Long Short-Term Memory networks (LSTMs) and convolutional neural networks (CNNs) can better capture nonlinear, time-dependent usage patterns [96],[104].”
From Economic Impacts and Mitigation for the Grid Connected Electric Vehicle Charging: A Technical and Operational Overview (2026) · cites this paper as [96]
In the citing paper
“If the charging occurs where there is a capacity deficit in the grid, a large-scale diffusion of EVs can cause grid issues [ [9][10][11]].”
From A large-scale high-resolution geographic analysis of impacts of electric vehicle charging on low-voltage grids (2022) · cites this paper as [9]
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Metadata
- Authors
- Muratori, M.
- Affiliations
- National Laboratory of the Rockies (US)
- Corresponding author
- Muratori, Matteo
- ORCID
- Muratori, Matteo
- Venue
- Nature Energy
- Published
- 2018-01-22
- Volume
- 3
- Issue
- 3
- Pages
- 193-201
- ISSN
- 2058-7546 (ISSN-L)
- OpenAlex
- W2789027126
- Cited by
- 695
- References
- 36
- Citations by year
- 2026: 104 · 2025: 120 · 2024: 86 · 2023: 105 · 2022: 71 · 2021: 99 · 2020: 62 · 2019: 35 · 2018: 12
- FWCI
- 19.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- plug-in electric vehicles, uncoordinated charging, peak load, electric mobility
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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