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Qubit Energy Schemas

License: MIT Schema Version Open-source JSON Schema definitions for energy data interchange. Built by the energy community, for the energy community.

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Vision

Creating a universal, open standard for energy data that enables seamless integration between energy assets, monitoring systems, and optimization platforms. Our schemas provide a common language for the energy transition.

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Core Schemas

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Foundation Schemas

Organization

Energy company, utility, or entity managing energy assets

Site

Physical location containing energy assets (solar farm, charging station)

Asset

Physical energy equipment (solar panels, batteries, inverters)

Meter

Energy measurement devices providing consumption/generation data

Sensor

Environmental or operational sensors (weather, temperature, vibration)

TimeSeries

Time-stamped measurements and telemetry data

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Advanced Schemas (v0.2)

Tariff

Dynamic pricing with carbon intensity, P2P trading, V2G rates

Event

VPP dispatches, blockchain verification, AI anomaly detection

Weather Observation

Hyperlocal weather with satellite, drone, and AI-enhanced data

Demand Response

ML baselines, V2G coordination, flexibility market participation

Alarm

Predictive maintenance alerts and cybersecurity threat detection

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Quick Start

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Installation

# Clone the schemas repository
git clone https://github.com/qubit-foundation/qubit-energy-schemas.git
cd qubit-energy-schemas

# Install validation dependencies
pip install -r requirements.txt

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Validate Example Data

python scripts/validate.py examples/site.json

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Use in Your Project

import json
from jsonschema import validate

# Load schema
with open('schemas/v0.2/site.json') as f:
    schema = json.load(f)

# Your data
site_data = {
    "id": "sit_solar_farm_001",
    "name": "North Solar Farm",
    "organization_id": "org_acme_energy",
    "location": {
        "latitude": 37.7749,
        "longitude": -122.4194,
        "timezone": "America/Los_Angeles"
    }
}

# Validate
validate(instance=site_data, schema=schema)
print("✅ Data is valid!")

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Schema Examples

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TimeSeries Schema

The most commonly used schema for real-time energy data: 
{
  "id": "ts_solar_farm_001_2024_01_15_14_30",
  "asset_id": "ast_solar_farm_001",
  "metric": "energy_generation",
  "value": 2500.0,
  "unit": "kWh",
  "timestamp": "2024-01-15T14:30:00Z",
  "quality": "good",
  "metadata": {
    "weather_condition": "sunny",
    "panel_temperature": 35.2,
    "inverter_efficiency": 0.98
  }
}

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Asset Schema

Defines physical energy equipment: 
{
  "id": "ast_battery_storage_001",
  "name": "Tesla Megapack 2XL",
  "site_id": "sit_energy_storage_facility",
  "type": "battery_storage",
  "specifications": {
    "capacity_kwh": 3916,
    "max_charge_rate_kw": 1890,
    "max_discharge_rate_kw": 1890,
    "efficiency": 0.89,
    "chemistry": "lithium_iron_phosphate"
  },
  "installation": {
    "commissioned_date": "2024-01-15",
    "warranty_years": 20,
    "installer": "Tesla Energy"
  }
}

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Key Features

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Schema Versions

Always specify which schema version you’re using. Version 0.2 includes significant enhancements for blockchain, AI, and advanced energy markets.

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Version 0.2 (Current)

  • Advanced tariff structures with dynamic pricing
  • Blockchain and smart contract integration
  • AI/ML metadata and confidence scoring
  • V2G and bidirectional energy flow support
  • Enhanced cybersecurity and threat detection

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Version 0.1 (Legacy)

  • Foundation schemas for basic energy data
  • Simple tariff structures
  • Core asset and measurement definitions

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Validation Tools

python scripts/validate.py examples/timeseries.json

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Contributing

1

Fork Repository

Fork the schemas repository
2

Create Branch

git checkout -b feature/new-schema-enhancement
3

Make Changes

  • Add or modify schema files in schemas/v0.2/
  • Include example data in examples/
  • Update documentation
4

Validate

python scripts/validate.py
pytest tests/
5

Submit PR

Create a pull request with detailed description of changes

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Best Practices

Always validate your data against the schemas before sending to production systems. Invalid data can cause downstream processing failures.

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Schema Design Guidelines

  1. Minimize Breaking Changes: Use optional fields for new features
  2. Clear Naming: Use descriptive, unambiguous field names
  3. Consistent Units: Always specify units explicitly
  4. Rich Metadata: Include context and provenance information
  5. Future-Proof: Design for extensibility and evolution

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Implementation Tips

Performance

Cache compiled schemas for repeated validation to improve throughput

Error Handling

Provide meaningful error messages when validation fails

Monitoring

Track validation success rates and common error patterns

Versioning

Support multiple schema versions during migration periods

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Real-World Examples

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Solar Farm Monitoring

{
  "id": "ts_solar_generation_2024_01_15_14_30",
  "asset_id": "ast_solar_array_001",
  "metric": "energy_generation",
  "value": 1250.5,
  "unit": "kWh",
  "timestamp": "2024-01-15T14:30:00Z",
  "quality": "good",
  "metadata": {
    "irradiance_wm2": 850,
    "ambient_temp_c": 25.3,
    "panel_temp_c": 45.1,
    "inverter_efficiency": 0.976
  }
}

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EV Charging Session

{
  "id": "ts_charging_power_2024_01_15_15_45",
  "asset_id": "ast_fast_charger_001",
  "metric": "charging_power",
  "value": 150.0,
  "unit": "kW",
  "timestamp": "2024-01-15T15:45:00Z",
  "quality": "good",
  "metadata": {
    "session_id": "ses_charge_001",
    "vehicle_id": "veh_tesla_model_s_001",
    "soc_percent": 65,
    "charging_curve": "fast"
  }
}
The Qubit Energy Schemas provide the data foundation that enables interoperability across the entire energy ecosystem. By establishing universal standards, we accelerate the development and deployment of innovative energy solutions.