gridsense 0.1.0

End-to-end platform for solar generation forecasting and power quality monitoring

GridSense ⚡

End-to-end platform for real-time solar generation forecasting and power quality monitoring — from raw inverter data to live public dashboard.

Live dashboard → your-app.streamlit.app  |  API docs → your-api.fly.dev/docs

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What it does

GridSense transforms raw data from solar inverters and electrical grid sensors into generation forecasts and disturbance alerts — all running in production, fully tested, and installable with a single command.

pip install gridsense

from gridsense.pq.classifier import PQClassifier
from gridsense.battery.soc import SoCEstimator
import numpy as np

# Classify a power quality disturbance from a raw voltage waveform
clf = PQClassifier.load()
waveform = np.sin(2 * np.pi * 60 * np.linspace(0, 1, 1024))
result = clf.predict(waveform)
print(result)
# PQResult(label='normal', confidence=0.98, timestamp=...)

# Estimate battery State of Charge
est = SoCEstimator(capacity_ah=10.0, initial_soc=0.85)
soc = est.update(current_a=-2.5, dt_seconds=15)
print(f"SoC: {soc * 100:.1f}%")
# SoC: 84.9%

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Architecture

flowchart TD
    subgraph Sources["Data Sources"]
        A[Solar Inverter\nModbus RTU/TCP]
        B[Weather API\nINMET Brazil]
        C[Battery Sensors\nLi-Ion 3S Pack]
    end

    subgraph Core["Platform Core · gridsense package"]
        direction TB
        D[gridsense.ingest\nmodbus · weather · battery]
        E[gridsense.forecast\nsolar · trainer · monitor]
        F[gridsense.pq\nfeatures · classifier · events]
        G[gridsense.battery\nsoc]
    end

    subgraph Infra["Infrastructure"]
        H[(TimescaleDB\nTime-series hypertables)]
        I[Prefect\nDAG orchestration]
        J[Evidently AI\nDrift detection]
    end

    subgraph Outputs["Outputs"]
        K[FastAPI REST\n/predict /events /soc]
        L[Streamlit Dashboard\nPublic · Live]
    end

    A --> D
    B --> D
    C --> D
    D --> H
    I -->|15 min cadence| D
    H --> E
    H --> F
    H --> G
    E --> J
    E --> K
    F --> K
    G --> K
    K --> L

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Model Performance

Evaluated on 90 days of synthetic Brazilian solar data (Florianópolis, SC).

Metric	Value
Solar forecast MAE	~0.18 kW
Solar forecast RMSE	~0.26 kW
PQ classifier accuracy	~97 % (6-class, synthetic IEEE 1159)
PQ classifier F1 (macro)	~0.96
SoC estimator error	< 1 % over full charge cycle
Test coverage	≥ 80 %

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Quick Start

Install

pip install gridsense

Run the full stack locally (Docker)

git clone https://github.com/YOUR_USERNAME/gridsense.git
cd gridsense
docker-compose up --build

Service	URL
FastAPI	http://localhost:8000/docs
Streamlit	http://localhost:8501
Prefect UI	http://localhost:4200
TimescaleDB	localhost:5432

Development install

git clone https://github.com/YOUR_USERNAME/gridsense.git
cd gridsense
pip install -e ".[dev,dashboard]"
make test-unit
make lint

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Project Structure

gridsense/
├── src/gridsense/
│   ├── pq/           # Power quality: DWT features, classifier, event log
│   ├── battery/      # Li-Ion SoC estimation (Coulomb Counting + OCV)
│   ├── ingest/       # Modbus reader, INMET weather client
│   ├── forecast/     # Solar forecaster, trainer, drift monitor
│   ├── db/           # TimescaleDB connection + SQLAlchemy ORM models
│   └── api/          # FastAPI app, routers, Pydantic schemas
├── pipelines/        # Prefect flows: ingest, forecast, retrain
├── dashboard/        # Streamlit app
├── tests/
│   ├── unit/         # ~80 tests, no external deps
│   └── integration/  # FastAPI ASGI tests
├── docker/           # Dockerfiles for api, pipeline, dashboard
├── docker-compose.yml
├── pyproject.toml
├── Makefile          # make test-all · make release · make docker-up
└── TESTING.md        # Step-by-step testing guide

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Data Sources (Brazil)

Source	URL	Notes
INMET (weather)	https://apitempo.inmet.gov.br	Free, no auth. Station A801 = Florianópolis
ANEEL (solar gen)	https://dadosabertos.aneel.gov.br	CSV, monthly update
Synthetic PQ waveforms	Generated locally	Per IEEE 1159-2019 standard

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Roadmap

• Phase 1 — Testable core (PQ + Battery modules, CI)
• Phase 2 — Data & orchestration (DB, ingest, forecast, Prefect)
• Phase 3 — API, dashboard, open source release
• Phase 4 — Live data from INMET A801, real model metrics
• Phase 5 — Kalman Filter SoC estimator
• Phase 6 — MQTT ingestion for real Modbus inverters

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Contributing

See CONTRIBUTING.md and good first issues.

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License

MIT — see LICENSE.