oRaklE
1.0.2Multi-Horizon Electricity Demand Forecasting in High Resolution
Overview
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.
Install
Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-05-0213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-2511 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 22.0 median / 43 max
- Test cases
- 1 / 0.00 per code line
Test coverage
Line coverage
0%
Expression
0.0%
Tests / Examples
0.0% / 16% ex
Functions
18 18 exported
Complexity
23.6 avg / 43 max
Call network
18 nodes / 17 edges
Call graph
Open call graph →Lowest coverage
18 functions| Function | Cyclo | Coverage |
|---|---|---|
| add_holidays_mid_term exp | 9 | 0% |
| add_holidays_short_term exp | 9 | 0% |
| combine_models exp | 30 | 0% |
| combine_models_future exp | 30 | 0% |
| decompose_load_data exp | 34 | 0% |
| fill_missing_data exp | 19 | 0% |
Datasets
| Name | Class | Rows × Cols | Also ships in |
|---|---|---|---|
| example_demand_data | data.frame | 43,769 × 7 | – |
| example_demand_data_filled | data.frame | 43,824 × 7 | – |
| example_full_model_future_predictions | data.frame | 105,120 × 12 | – |
| example_full_model_predictions | data.frame | 43,800 × 12 | – |
| example_longterm_and_macro_data | data.frame | 16 × 14 | – |
| example_longterm_data | data.frame | 16 × 4 | – |
| example_longterm_future_macro_data | data.frame | 23 × 18 | – |
| example_longterm_future_predictions | data.frame | 23 × 18 | – |
| example_longterm_predictions | data.frame | 16 × 18 | – |
| example_midterm_demand_data | data.frame | 1,825 × 10 | – |
| example_midterm_future_predictions | data.frame | 4,380 × 46 | – |
| example_midterm_predictions | data.frame | 1,825 × 46 | – |
| example_shortterm_demand_data | data.frame | 43,800 × 14 | – |
| example_shortterm_future_predictions | data.frame | 105,120 × 40 | – |
| example_shortterm_predictions | data.frame | 43,800 × 40 | – |
| weo_data | tbl_df/tbl/data.frame | 588 × 61 | – |
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.2Latest
- 1.0.12025-05-05 · diff ↗
- 1.0.02025-04-29
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-04-29
- Total releases
- 3 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 4.4 MB / 18 files
- Download size
- 4.4 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet