🏗️ What is EnergyDB?

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

Layer	Description	Real-World Example
🌳 Asset hierarchy	Arbitrary-depth tree of portfolios, sites, and assets	"Offshore-1 → WindTurbine T01 → power"
🔗 Grid topology	Typed edges (lines, transformers, pipes, interconnections) connecting any two assets	"Cable-1: BusA → BusB"
⏱️ 3-dimensional time series	Actuals and versioned forecasts attached to any node or edge, queryable as-of any point in time	"power_flow on Cable-1, valid Wed 12:00, known Mon 18:00"

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.

EnergyDB 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 as a TreeDiff before you apply — no surprise mutations, and the same preview is available across a whole transaction().
• ⏱️ 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.
• 🧹 Idempotent writes: write(..., skip_unchanged=True) drops rows that only duplicate the latest stored value before insert, so writing the same window repeatedly doesn't bloat storage. The comparison key is chosen per series — actuals dedupe per valid_time, versioned forecasts per (valid_time, knowledge_time) — so a republished forecast is never mistaken for a duplicate. Opt-in; the write still returns its run_id (a WriteResult carrying .written / .skipped counts).
• 🎯 Partial reads that don't fail: read(manifest, on_missing="skip") returns every series that resolved plus the triples that didn't, so one unregistered series in a 1,500-series manifest costs you that series — not the whole batch.
• 🧯 Typed errors: NodeNotFoundError, SeriesNotFoundError (carrying every unresolved triple), ManifestError, … all under one EnergyDBError base — so you branch on types and structured fields instead of matching message text. Every class also subclasses ValueError, so broad handlers keep working.

---

---

🚀 Quick Start

1. Installation

pip install energydb

Requires Python 3.12+, PostgreSQL (asset hierarchy + series catalog), and ClickHouse (time-series values).

Need a local Postgres + ClickHouse? One command brings both up: cd local-db && docker compose up -d (see local-db/, or DEVELOPMENT.md for the full setup).

2. Usage Example

from datetime import UTC, datetime
import energydb as edb
import pandas as pd

client = edb.Client()  # reads TIMEDB_PG_DSN / TIMEDB_CH_URL
client.create()

# 1. Declare your portfolio: a tree of typed assets with their series.
t01 = edb.wind.WindTurbine(
    name="T01", capacity=3.5, hub_height=80,
    timeseries=[edb.TimeSeries(name="power", unit="MW",
                               data_type=edb.DataType.ACTUAL)],
)
portfolio = edb.Portfolio(
    name="my-portfolio",
    members=[edb.Site(name="Offshore-1", members=[t01])],
)
client.register_tree(portfolio)   # create-only; edit existing nodes via scope mutators

# 2. Write hourly power for that turbine.
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",
)

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

Async? edb.Client is a synchronous facade over edb.AsyncClient. For async/await code, use AsyncClient directly — await client.open() once, then await every method shown above.

---

🧪 Try it in Google Colab

Want to try EnergyDB without a local setup? Open our Quickstart in Colab — the first cell automatically installs PostgreSQL + ClickHouse inside the VM.

Note: Data persists only within the active Colab session. Additional notebooks are available in the examples/ directory.

---

📚 Documentation & Resources

• 📖 Official Documentation
• ⚙️ Installation Guide
• 🐍 Python SDK Documentation
• 🌐 Reference
• 💡 Examples & Notebooks

---

📦 Related Projects

Project	Description
TimeDB	3-dimensional time-series storage on ClickHouse with auditability and overlapping-forecast support
TimeDataModel	Pythonic data model for time series
EnergyDataModel	Data model for energy assets (solar, wind, battery, grid, ...)

---

🤝 Contributing

Contributions are welcome! If you're interested in improving EnergyDB, please see our Development Guide for local setup instructions.

---