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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "oRaklE" in publications use:'
type: software
license: MIT
title: 'oRaklE: Multi-Horizon Electricity Demand Forecasting in High Resolution'
version: 1.0.2
doi: 10.32614/CRAN.package.oRaklE
abstract: Advanced forecasting algorithms for long-term energy demand at the national
  or regional level. The methodology is based on Grandón et al. (2024) <https://doi.org/10.1016/j.apenergy.2023.122249>;
  Zimmermann & Ziel (2024) <https://doi.org/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.
authors:
- family-names: Schwenzer
  given-names: Johannes
  email: schwenzer@europa-uni.de
  orcid: https://orcid.org/0009-0006-9618-8889
- family-names: Maxand
  given-names: Simone
  email: maxand@europa-uni.de
  orcid: https://orcid.org/0000-0002-3153-7922
- family-names: Gonzalez Grandón
  given-names: Tatiana
  email: tatiana.c.g.grandon@ntnu.no
  orcid: https://orcid.org/0000-0001-6587-0144
repository: https://cran.r-universe.dev
commit: 39d94c0ffd0b7fb5560356d020784201a0168084
date-released: '2025-11-21'
contact:
- family-names: Schwenzer
  given-names: Johannes
  email: schwenzer@europa-uni.de
  orcid: https://orcid.org/0009-0006-9618-8889