Welcome to EnergyDB

EnergyDB is an open-source library for persisting full energy portfolios — assets, grid topology, and 3-dimensional time series — in one connected database backed by PostgreSQL and ClickHouse.

What is EnergyDB?

EnergyDB is a database for energy portfolios. It stores three things together in one connected system:

  • 🌳 An asset hierarchy — your fleet modeled as a tree (Portfolio → Site → WindTurbine, Battery, …) of arbitrary depth.

  • 🔗 Grid topology — typed edges (lines, transformers, pipes, interconnections) connecting any two assets, including across portfolios.

  • ⏱️ 3-dimensional time series — actuals and versioned forecasts attached to any node or edge, queryable as-of any point in time.

Structure lives in PostgreSQL, values live in ClickHouse, and stable UUID identity lets Python objects round-trip to the database without losing any structural state.

It extends TimeDB with persistent storage for EnergyDataModel hierarchies.

Why EnergyDB?

Most time-series systems are agnostic about what their series represent — they treat data as opaque (series_id, timestamp, value) triples. EnergyDB knows it is a portfolio, and links every series back to the asset or grid edge it describes.

  • 🔁 Round-trip persistence: every Element keeps its UUID7 from in-memory object to row primary key — renames, moves, and property edits become silent UPDATEs, never delete-then-insert.

  • 📋 Diffable structural changes: dry_run=True previews every insert, rename, move, and delete before you apply — no surprise data loss on replace_subtree.

  • ⏱️ Time-of-knowledge queries: forecast revisions, corrections, and as-of backtests, powered by TimeDB.

  • 🧭 Lazy fluent navigation: client.get_node("Portfolio", "Site", "T01").read(...) resolves to one indexed SQL query, regardless of subtree size.

  • ⚖️ Unit conversion at the boundary: declare canonical units once; pint rescales every read and write automatically.

Quick Start

pip install energydb

from datetime import UTC, datetime

import energydb as edb
import pandas as pd

client = edb.Client()  # reads TIMEDB_PG_DSN / TIMEDB_CH_URL from env
client.create()        # PostgreSQL schema + ClickHouse series_values table

# 1. Declare a turbine with the series it will hold (metadata only).
t01 = edb.wind.WindTurbine(
    name="T01", lat=55.01, lon=3.02, capacity=3.5, hub_height=80,
    timeseries=[
        edb.TimeSeries(name="power", unit="MW",
                       data_type=edb.DataType.ACTUAL),
    ],
)

# 2. Wrap it in a site and a portfolio.
site = edb.Site(name="Offshore-1", lat=55.0, lon=3.0, members=[t01])
portfolio = edb.Portfolio(name="my-portfolio", members=[site])

# 3. Persist structure — create-only. Edit existing nodes via scope mutators.
client.register_tree(portfolio)

# 4. Write a day of hourly values for the turbine's power series.
start = datetime(2026, 1, 1, tzinfo=UTC)
df = pd.DataFrame({
    "valid_time": pd.date_range(start, periods=24, freq="1h", tz="UTC"),
    "value": [2.5 + 0.05 * i for i in range(24)],
})
client.get_node("my-portfolio", "Offshore-1", "T01").write(
    df, name="power", data_type="actual",
)

# 5. Read back — single asset.
client.get_node("my-portfolio", "Offshore-1", "T01").read(
    name="power", data_type="actual",
)

# Or across the whole portfolio in one fluent call.
client.get_node("my-portfolio").read(name="power", data_type="actual")

Release Notes

For version-by-version changes and migration notes, see:

Documentation

Contents:

Indices and tables