Beginner-Friendly Review of Research on R-Based Energy Forecasting: Insights from Text Mining
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| Publicado en: | Electronics vol. 14, no. 17 (2025), p. 3513-3572 |
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| Otros Autores: | , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14173513 |2 doi | |
| 035 | |a 3249684852 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Kim Minjoong |u Department of ICT Convergence, Soonchunhyang University, Asan 31538, Republic of Korea; wooni3804@sch.ac.kr | |
| 245 | 1 | |a Beginner-Friendly Review of Research on R-Based Energy Forecasting: Insights from Text Mining | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Literature Review | ||
| 520 | 3 | |a Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. | |
| 653 | |a Sparsity | ||
| 653 | |a Energy management | ||
| 653 | |a Software | ||
| 653 | |a Accessibility | ||
| 653 | |a Collaboration | ||
| 653 | |a Forecasting | ||
| 653 | |a Trends | ||
| 653 | |a Data mining | ||
| 653 | |a Modelling | ||
| 653 | |a Python | ||
| 653 | |a Data science | ||
| 653 | |a Machine learning | ||
| 653 | |a Time series | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Energy consumption | ||
| 653 | |a Climate change | ||
| 653 | |a Programming languages | ||
| 653 | |a Gas pipelines | ||
| 653 | |a Thermal energy | ||
| 653 | |a Engineering | ||
| 653 | |a Natural language processing | ||
| 653 | |a Folklore | ||
| 653 | |a Literature reviews | ||
| 653 | |a Natural gas | ||
| 653 | |a Building management systems | ||
| 653 | |a Ensemble learning | ||
| 653 | |a Reproducibility | ||
| 653 | |a Independent study | ||
| 700 | 1 | |a Kim Hyeonwoo |u Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Republic of Korea | |
| 700 | 1 | |a Moon Jihoon |u Department of Data Science, Duksung Women’s University, Seoul 01369, Republic of Korea | |
| 773 | 0 | |t Electronics |g vol. 14, no. 17 (2025), p. 3513-3572 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3249684852/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3249684852/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3249684852/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |