---
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

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.

- 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

## Files

- `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

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.

## Citation

NM AI Research. AI Energy-Demand Forecast Scorecard. Zenodo. https://doi.org/10.5281/zenodo.20572928 . Licensed CC BY 4.0.