Cited by 711Smart Grid and Power SystemsSmart Grid Energy ManagementEnergy Load and Power Forecasting

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

Ahmad, Tanveer, Madonski, Rafal, Zhang, Dongdong, Huang, Chao, Mujeeb, Asad·2022·Renewable and Sustainable Energy Reviews

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Crossref · 10.1016/j.rser.2022.112128

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150 of 150 references verified0 citation errors · 0 not checkable

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Authors
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A.
Affiliations
  • Jinan University (CN)
  • Guangxi University (CN)
  • University of Science and Technology Beijing (CN)
  • Tsinghua University (CN)
Corresponding author
Ahmad, Tanveer
Venue
Renewable and Sustainable Energy Reviews
Publisher
Elsevier BV
Published
2022-05
Volume
160
Pages
112128
ISSN
1364-0321 (ISSN-L)
OpenAlex
W4220842087
Cited by
711
References
150
Citations by year
2026: 104 · 2025: 280 · 2024: 212 · 2023: 92 · 2022: 23
FWCI
41.57
Type
journal article
Language
en
Open access
closed
Locations
  • Renewable and Sustainable Energy Reviews (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
Keywords
probabilistic machine learning, smart grid, energy distribution networks, energy forecasting, energy price forecasting, merit order, renewable energy integration, energy storage, energy efficiency, energy materials, big data analytics, cybersecurity, distributed energy resources, power transmission, energy planning, load forecasting

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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.

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