Multi-Horizon Electricity Demand Forecasting in High Resolution R package oRaklE version 1.0.2 Advanced forecasting algorithms for long-term energy demand at the national or regional level. The methodology is based on Grandón et al. (2024) <doi:10.1016/j.apenergy.2023.122249>; Zimmermann & Ziel (2024) <doi:10.1016/j.apenergy.2025.125444>. Real-time data, including power demand, weather conditions, and macroeconomic indicators, are provided through automated API integration with various institutions. The modular approach maintains transparency on the various model selection processes and encompasses the ability to be adapted to individual needs. 'oRaklE' tries to help facilitating robust decision-making in energy management and planning. Software Depends: R (>= 3.5) Imports: ggplot2, scales, MLmetrics, MuMIn, R.utils, caret, survival, countrycode, doParallel, dplyr, ggthemes, glmnet, httr, jsonlite, lubridate, mgcv, patchwork, purrr, xml2, zoo Suggests: knitr, rmarkdown, roxygen2 (>= 7.2.3), spelling, testthat (>= 3.0.0) Johannes Schwenzer <schwenzer@europa-uni.de> Comprehensive R Archive Network (CRAN) Johannes Schwenzer [aut, cre, cph] (ORCID: <https://orcid.org/0009-0006-9618-8889>), Simone Maxand [aut] (ORCID: <https://orcid.org/0000-0002-3153-7922>), Tatiana Gonzalez Grandón [aut] (ORCID: <https://orcid.org/0000-0001-6587-0144>) MIT + file LICENSE (https://CRAN.R-project.org/package=oRaklE/LICENSE) 2025-11-21 application/tgz https://CRAN.R-project.org/package=oRaklE doi:10.32614/CRAN.package.oRaklE en-US