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

Full API-level detail — every capability, its flags, and the standard it cites — is in docs/CAPABILITIES.md.

• Ingest — per-point and wide/tabular CSV, a long/tall adapter, named vendor profiles and a multi-format timestamp/value parser (ISO / US / EU-dayfirst / BAS / epoch / Excel-serial), a Project-Haystack hisRead client, SQL/historian readers, and read-only network adapters (Modbus, MQTT/Sparkplug, BACnet incl. experimental BACnet/SC, OPC-UA) — read-only by construction, lazy-imported, historian-first (SECURITY).
• Semantic model — a vendor-neutral Role vocabulary + mapping provider, a site/equipment/point entity model with completeness validation, a served-by topology populated from Brick, Haystack or naming, and interop both ways with Brick, Haystack tags and ASHRAE 223P.
• FDD — ASHRAE Guideline 36 AFDD (operating states, FC#1–15, trim-and-respond resets) and PNNL Building Re-tuning diagnostics; an 11-rule central plant & hydronic library; packaged/DX and refrigerant-side rules (RTU, heat-pump/VRF, DOAS, FCU); a Sequence-of-Operations conformance engine with a packaged G36 clause library; cohort/peer and topology-scoped fleet rules.
• Drift detection — the complement to "is this value wrong?": has this equipment been drifting away from its own frozen baseline? Six families — chiller, condenser, evaporator, pump/hydronic, AHU air-side and VAV zone-terminal — each comparing at matched load or duty, each rolling up to one localized verdict, and each driveable from a config or camber drift (CLI).
• Trim-and-Respond / G36 reset analytics — does the plant's reset logic do what G36 intends? Reset compliance and effectiveness, plus a rogue-zone census (which zone monopolizes the reset) and its common-mode twin, cohort starvation. See TR-RESET.md.
• Data trust — sensor faults are not equipment faults: physical bounds, cross-sensor consistency, sensor drift vs an external reference, and mapping confidence, wired as a gate so a rule that cannot trust its inputs declines to fire rather than reporting a false fault.
• M&V — IPMVP Options A / B / C / D: change-point models (2P–5P + zero variants), LBNL TOWT, G14 fit statistics and fractional savings uncertainty, CUSUM, weather normalization, normalized annual savings, non-routine adjustment, retrofit isolation, variable-base degree-day, and a 1R1C/2R2C grey-box calibrated to metered energy (OPTION-D). CalTRACK-aligned.
• Commissioning — RCx/MBCx: functional-test scoring, before/after persistence checks, and a measure register grading each fix to verified / regressed / inconclusive.
• Money & compliance — a native tariff engine + OpenEI URDB, bill validation, ECM NPV/IRR/SIR, demand & peak analytics, per-fault dollar economics, and BPS / EUI compliance checks.
• Grid & carbon — demand response and flexibility quantification, carbon-aware load timing, hourly/marginal Scope-2, and OpenADR export.
• Domain analytics — Std-55 comfort, CO₂/62.1 ventilation, cost, carbon, water, load profiling and disaggregation, schedule inference, PV (+ pvlib), psychrometrics (+ PsychroLib), lighting.
• Weather — two keyless providers (NASA POWER's global grid, NOAA/ISD's real stations) plus a geocoder, so you can fetch outdoor conditions by address (WEATHER).
• Advisory & synthesis — impact prioritization, root-cause grouping, fault-lifecycle tracking, advisory setpoint/sequence suggestions (ASO), action plans, and a building health scorecard.
• AI-assist (advisory, provider-agnostic) — assisted point mapping and grounded explanation and Q&A over the deterministic layers, citing the rule and data behind every claim. Fully useful with no LLM wired; no vendor named, no SDK, no network (AGENT).
• Reporting & visualization — ASHRAE/ACCA Standard 211 audits, a portfolio rollup ranked by recoverable dollars, ten chart patterns where every rule renders its own evidence, a self-contained HTML dashboard with cross-panel brush linking, and a live web UI.
• Storage & platform — a partitioned Parquet store with rollups, retention and a cached catalog (validated to portfolio scale), a plugin API, findings → CMMS + notifiers, a read-only HTTP API, and a one-way edge forwarder for cybersecure edge→cloud collection.
• Validation — accuracy scored against labeled public data and CI-gated, plus camber validate, a single credibility dossier. Honest about its limits: VALIDATION.md states per detector family which claims rest on real data and which are synthetic-only.

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)

The core is dependency-light (numpy / pandas / pyarrow / matplotlib). Everything else is an optional extra, lazy-imported so the core never pays for it: brick, haystack, modbus, mqtt, bacnet, opcua, pv, psychro, tariff, ml, energyplus, docs, dev. Install what you use.

pip install -e .            # the package (editable)
pip install -e .[dev]       # + pytest / ruff / mypy, for development

Quickstart

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

CAMBER installs a camber console script. A whole analysis is one JSON config (source → mapping → equipment → rules → report) and one command:

camber run    config.json --out out/   # discover equipment, run the rules, write findings.json
camber report config.json --out audit.html
camber ask    "which building is worst?" --config config.json   # grounded, cited
camber serve  ./store                  # read-only API + live dashboard at /ui

Drift detection needs a frozen reference, so it has its own verbs — see docs/CLI.md and the runnable examples/drift/ walkthrough:

camber drift freeze config.json        # establish the baselines (the only create path)
camber drift run    config.json        # score the current window against them

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 in one JSON config and run it without a script: camber run config.json (or python -m camber.config config.json). Add a drift section and the same command scores baseline-vs-current drift alongside the rules.

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 sources are wired as runnable examples (referenced + fetched, not bundled):

• LBNL Fault Detection and Diagnostics Datasets (CC-BY) — labeled equipment data. examples/lbnl_fdd/ maps its point names to roles, validates completeness, round-trips through the Parquet store, and scores the detector suite across five wired subsets: single-duct AHU, fan-coil unit, dual-duct AHU, the VAV fan-power-unit set (--fpu) and the chiller-plant set (--chiller). Which detectors each subset can honestly test — and which stay synthetic-only — is stated in VALIDATION.md.
• 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/CAPABILITIES.md for a full capability reference (API + option flags per feature), 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.

Community & support

Usage questions and ideas: GitHub Discussions. Bugs and concrete feature requests: Issues. Please keep everything vendor- and site-neutral — describe scenarios generically and never post a real client site name or raw building data.

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). The public surface is settled and locked by a snapshot test, and changes follow the deprecation policy in docs/API-STABILITY.md — but until 1.0 it can still move.