Reports

openenergy report turns saved outputs into one HTML file of charts and tables. It reads what the other commands wrote, so nothing is re-run and any past run can be reported.

uv sync --extra report      # or: pip install 'openenergy[report]'  (plotly)
uv run openenergy run examples/gb-2019-naive.yaml --out outputs/naive
uv run openenergy run examples/gb-2019-perfect.yaml --out outputs/perfect
uv run openenergy report outputs/naive outputs/perfect -o outputs/report.html

See the example report for GB 2019 (a battery on real prices and the system model), built from the bundled data when these docs are built.

What goes in

Input	Written by	Sections
A run directory	openenergy run	Key figures; cumulative revenue against perfect foresight; revenue by month; average day; stored energy by day and hour; price and forecast; forecast error by hour; revenue by market, plant output and emissions where the run has them
A system directory	openenergy system run	Key figures; generation by month; price duration curve; mean price by hour; curtailment and emissions by month; battery figures when present
A backcast directory	openenergy system validate --out	The per-year table; model and actual price by hour and by month; duration curves; gas and coal by year
A sweep CSV	openenergy sweep --out	Results table; ranking; revenue per MW-year against each swept setting

The kind is detected from the files. Several run directories also get a comparison section (key figures side by side and cumulative revenue on one chart), as do several system directories (duration curves and price by hour together).

Options

Option	Default	Meaning
-o, --out	report.html	File to write
--title	OpenEnergy report	Title at the top of the page
--cdn	off	Load plotly.js from its CDN: about 0.4 MB per chart instead of a 5 MB self-contained file, but the report then needs internet to show charts

By default the report is self-contained and opens offline. Charts are interactive: zoom, pan, hide a series by clicking its legend entry, or save a chart as PNG from its toolbar.

Numbers and sources

Key figures come from each run's summary.json exactly as written, so they match what the command printed. Each section repeats the run's notes and data attribution, and the footer states the licences; share the report with those intact. openenergy run also writes benchmark_daily.csv, the perfect-foresight benchmark by day, which the revenue chart uses.

In Python

The charts are plain functions from pandas to plotly figures, so they work in notebooks and apps too:

from openenergy.report import build_report, load_output
from openenergy.report import figures

run = load_output("outputs/naive")
figures.state_of_charge(run.intervals).show()
html = build_report([run], title="Naive 2019")