robikscube/hourly-energy-consumption: papers linking to it

Kaggle · www.kaggle.com/datasets/robikscube/hourly-energy-consumption

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to robikscube/hourly-energy-consumption (https://www.kaggle.com/datasets/robikscube/hourly-energy-consumption), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the Kaggle dataset robikscube/hourly-energy-consumption in their Crossref records, the first in 2022; most of them (1) in 2026.
By category: AI (2) and Operations Research (1); the journals with the most are Expert Systems with Applications (1), Neural Computing and Applications (1) and Mathematical Programming Computation (1).

By year#

20222026

Papers linking to robikscube/hourly-energy-consumption by publication year
Year	Papers
2022	1
2025	1
2026	1

Journals#

Journals whose papers link to robikscube/hourly-energy-consumption most
Journal	Papers
Expert Systems with Applications	1
Neural Computing and Applications	1
Mathematical Programming Computation	1

Papers linking to robikscube/hourly-energy-consumption, most cited first
Published	Title	Journal	Authors	Citations	Link
2022-10-19	A dynamic programming approach for generalized nearly isotonic optimization	Mathematical Programming Computation	Zhensheng Yu, Xuyu Chen, Xudong Li	7	cites it
2025-12-09	EnergyFormer: dual-supervised adaptive multi-scale Transformer for multistep energy forecasting	Expert Systems with Applications	Xiaorui Shao, Ruizhang Huang, Yongbin Qin	3	cites it
2026-07-01	Hybrid machine learning approach for short-term load forecasting in smart grids	Neural Computing and Applications	Amnah Alhussain, S. M. Suhail Hussain	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). robikscube/hourly-energy-consumption: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-robikscube-hourly-energy-consumption

@misc{scholar4ai-code-kaggle-robikscube-hourly-energy-consumption,
  author = {{Scholar4AI}},
  title = {robikscube/hourly-energy-consumption: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-robikscube-hourly-energy-consumption}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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