metadata
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.