camber-toolkit 0.1.0

Vendor-neutral building-automation-system trend analysis: FDD, M&V, and retro-commissioning

CAMBER

Commissioning, Analytics & M&V for Building Energy Re-tuning

A vendor-neutral Python toolkit for analyzing Building Automation System (BAS) trend data — fault detection & diagnostics (FDD), measurement & verification (M&V), and retro-commissioning (RCx) — across any building, independent of the BAS vendor.

The core idea: points are mapped to a small vocabulary of vendor-neutral roles (HEAT_VALVE, SUPPLY_AIR_TEMP, OAT, …), and every diagnostic is written against those roles. Map a building's tags once and the whole rule set runs on it — one rule, all equipment, any BAS.

What it does

• Ingest — adapters for per-point CSV exports, wide/tabular CSV, and a Project Haystack hisRead client (behind an injectable transport).
• Semantic model — a role vocabulary + mapping provider, plus a site/equipment /point entity model with completeness validation (which analytics a building's instrumentation can support). Brick interop: derive the point→role mapping automatically from a building's Brick (.ttl) model.
• FDD — a rule engine implementing ASHRAE Guideline 36 AFDD (operating states, fault conditions FC#1–15, trim-and-respond resets) and PNNL Building Re-tuning diagnostics (simultaneous heat/cool, reheat penalty, SAT/CHW reset, economizer, boiler lockout, overcooling, setback, OA fraction incl. under-ventilation, leaking valves, static/pump resets). Plus impact prioritization, fault-lifecycle tracking, and an FDD-accuracy benchmark harness (scored against labeled data).
• M&V — IPMVP / ASHRAE Guideline 14 change-point inverse models (2P–5P plus heating-zero variants) and the LBNL Time-of-Week & Temperature (TOWT) model for schedule-driven loads, CV(RMSE)/NMBE statistics and fractional savings uncertainty, CUSUM savings tracking, weather normalization (TMY/EPW), and rate/energy-aware resampling.
• Cost & carbon — utility cost accounting (energy + demand + time-of-use, and tiered water/wastewater with marginal vs. average rates) and GHG emissions (eGRID/EIA factors → CO₂e and intensity).
• Water — irrigation water budgets (ETo / landscape coefficient / efficiency / effective precipitation), cooling-tower makeup (cycles of concentration, gal/ton-hr), and leak detection (minimum night flow, flow duration, leak cost).
• Load profiling — peak / near-peak / base, base-to-peak ratio, load factor, load-duration curve, and weekday/weekend daily shapes.
• On-site systems — PV performance ratio, specific yield, and net/self- consumption energy; lighting operational efficiency vs. installed power with controls-fault flags (failed setback, no turndown).
• Comfort — Fanger PMV/PPD per ASHRAE Standard 55 / ISO 7730.
• Reporting — ASHRAE/ACCA Standard 211 audit deliverables (benchmark + prioritized findings + ECM table) to text/HTML, and a fleet/portfolio rollup (cross-sectional EUI benchmarking + fleet-wide fault counts).
• Storage — a Parquet time-series store keyed to the entity model, with tag-filtered reads.
• Quality — per-point data-quality scoring (robust outliers, flatline/gap detection) with an auditable cleaning trail.
• Integration — findings → CMMS-ticket records and a pluggable notifier; a read-only HTTP API over the store (python -m camber.api.server <store> [port]: /sites, /points, /history).

Install

Python 3.10+. The PyPI distribution name is camber-toolkit (it imports as camber).

pip install camber-toolkit             # from PyPI
pip install "camber-toolkit[brick]"    # + rdflib, for robust Brick-model parsing (optional)

From source (for development):

pip install -e .            # the package (editable)
pip install -e .[dev]       # + pytest, for development
pip install -e .[brick]     # + rdflib, for robust Brick-model parsing (optional)
pip install -e .[haystack]  # + a Haystack client (phable / pyhaystack) (optional)

Quickstart

python -m pytest -q                 # run the test suite
python examples/synthetic_demo.py   # data-free FDD demo on generated trends

Usage

Everything runs on role-named frames — a DataFrame whose columns are vendor-neutral Roles. Map a building's tags to roles once, then every diagnostic and model runs on it.

Fault detection — run a diagnostic, get a structured Finding:

import numpy as np, pandas as pd
from camber.model.roles import Role
from camber.rules.simul_hc import SimultaneousHeatCool

idx = pd.date_range("2025-07-07", periods=24 * 7, freq="1h")
frame = pd.DataFrame({
    Role.OAT:        90 + 10 * np.sin((idx.hour - 9) / 24 * 2 * np.pi),
    Role.COOL_VALVE: 70.0,                                  # cooling all day
    Role.HEAT_VALVE: np.where((idx.dayofweek < 5) & idx.hour.isin([11, 12, 13, 14]),
                              40.0, 0.0),                    # midday reheat — a fault
}, index=idx)

f = SimultaneousHeatCool().analyze("AHU_1", frame)
print(f.severity, f.metrics["simultaneous_hc_pct"])         # -> fault 36.36

Measurement & verification — fit a change-point baseline and score it:

import numpy as np
from camber.mandv.models import best_model, N_PARAMS
from camber.mandv.stats import fit_stats

oat = np.linspace(35, 100, 120)
energy = 50 + np.clip(oat - 65, 0, None) * 3 + np.random.default_rng(0).normal(0, 2, 120)
m = best_model(oat, energy)                                 # picks the inverse model
st = fit_stats(energy, m.predict(oat), N_PARAMS[m.kind])
print(m.kind, round(st.r2, 2), f"{st.cv_rmse:.0%}")         # -> 3PC 1.0 2%

Your own building — map point names → roles in a small JSON config (or derive it from a Brick model with camber.interop.brick), then resolve() assembles the role-frames. See examples/ for end-to-end runs on public datasets.

Reproducible runs — describe a whole analysis (source → mapping → equipment → rules → report) in one JSON config and run it without a script:

python -m camber.config run.json     # discovers equipment, runs the rules, writes the report

Docker

docker build -t camber .
docker run --rm camber               # runs the test suite as a clean-build proof
docker run --rm -it camber bash      # interactive shell

Mount a building's CSV export at /data to run analytics on real trends.

Public datasets

The toolkit is data-agnostic. Two open datasets are wired as runnable examples (referenced + fetched, not bundled):

• LBNL Fault Detection and Diagnostics Datasets (CC-BY) — labeled single-duct-AHU data. examples/lbnl_fdd/ maps its point names to roles, validates completeness, round-trips through the Parquet store, and detects a labeled stuck-damper fault (OAF ~21% baseline → ~100% fault).
• Building Data Genome Project 2 (CC-BY) — 3,053 whole-building hourly meters. examples/bdg2/ fits the G14/IPMVP change-point engine (textbook 3PC on cooling energy, R² 0.78–0.94) and ingests the portfolio into the store.

Each example has a fetch.py (downloads to the git-ignored examples/_data/) and a runnable script. See the per-example READMEs.

Contributing

Contributions are welcome — new diagnostics, ingest adapters, M&V models, ontology interop, docs, and fixes. See CONTRIBUTING.md for the dev setup and conventions, docs/ARCHITECTURE.md for the layered design, ROADMAP.md for what's planned and where to help, docs/ECOSYSTEM.md for the OSS-integration strategy, and the Code of Conduct. Security reports: see SECURITY.md.

Provenance

This is a clean-room implementation. Algorithms are reimplemented from public standards — ASHRAE Guideline 36, Guideline 14, Standard 55, Standard 211; IPMVP; PNNL Building Re-tuning; NIST APAR. No third-party source code is included.

License

Apache-2.0. See LICENSE and NOTICE.

Status: pre-release (v0.x). APIs may change.