Short-Term Load Forecasting Using Random Forests
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Random forest is an additive method based on several decision trees which has been widely proved to be a powerful approach to forecast electrical load.
“RF is an additive method based on several decision trees which has been widely proved to be a powerful approach to forecast electrical load [40].”
From Seasonality Effect Analysis and Recognition of Charging Behaviors of Electric Vehicles: A Data Science Approach (2020) · cites this paper as [40]
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
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Cited by
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Dominguez-Jimenez, J. A., Campillo, J. E., Montoya, O. D., Delahoz, E., & Hernández, J. C. (2020) · Sustainability · doi:10.3390/su12187769 · Cited by 28 · see citing works
“RF is an additive method based on several decision trees which has been widely proved to be a powerful approach to forecast electrical load [40].”
Method
- Authors
- Dudek, G.
- Affiliations
- Częstochowa University of Technology (PL)
- Corresponding author
- Dudek, Grzegorz
- ORCID
- Dudek, Grzegorz
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 821-828
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W2174096604
- Cited by
- 214
- References
- 1
- Citations by year
- 2026: 7 · 2025: 20 · 2024: 28 · 2023: 31 · 2022: 26 · 2021: 22 · 2020: 32 · 2019: 21 · 2018: 12 · 2017: 5 · 2016: 7 · 2015: 2 · 2014: 1
- FWCI
- 17.42
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- short-term load forecasting, electricity load forecasting, random forest, ensemble learning, time series forecasting, non-stationarity, heteroscedasticity, forecast accuracy comparison
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.
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