| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - ai |
| - energy |
| - data-centers |
| - forecasting |
| - reproducible-research |
| pretty_name: AI Energy-Demand Forecast Scorecard |
| --- |
| |
| # AI Energy-Demand Forecast Scorecard |
|
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| A reproducible audit of how the field forecasts data-centre electricity demand: how the published forecasts disperse, how they get revised, and whether they are transparent enough to reproduce. Primary-sourced, published with the data and a script that regenerates every figure. |
|
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| - Author: NM AI Research (independent analyst) |
| - ORCID: 0009-0003-4213-7769 |
| - DOI: https://doi.org/10.5281/zenodo.20572928 |
| - Interactive tool: https://nmairesearch.github.io/forecast-scorecard/ |
| - Source and code: https://github.com/NMAIResearch/forecast-scorecard |
|
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| ## Files |
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| - `forecast_scorecard_data.csv` (11 rows): one row per published forecast. Columns: `forecaster`, `forecast_date`, `horizon_year`, `scope`, `metric`, `unit`, `value_low`, `value_high`, `revised_value`, `revised_date`, `transparency`, `verified`, `source`, `notes`. |
| - `build.py`: standard-library reproducer that reads the data and writes the front-end. |
| - `LICENSE`: Creative Commons Attribution 4.0 International. |
|
|
| ## Method |
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| Dispersion is measured only within comparable slices, because units and scopes are not interchangeable. Each forecast is traced through a transparency funnel from verified to confirmable to reproducible. Drafting is AI-assisted; the judgement is not. |
|
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| ## Citation |
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| NM AI Research. AI Energy-Demand Forecast Scorecard. Zenodo. https://doi.org/10.5281/zenodo.20572928 . Licensed CC BY 4.0. |
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