Kitchen Performance Tests

This guide is scoped to Emerging Household Energy implementation. Kitchen Performance Tests (KPTs) measure and validate household fuel consumption through direct observation and structured evidence collection.

Methodological Requirements

Test Design

Random Selection:
- Households must be randomly selected
- Selection methodology must be documented in MADD
- Platform provides tools for random selection and documentation
Test Groups:
- Reference group (old stove users)
- Intervention group (new stove users)
- Both groups must be tested simultaneously
- Reference group must be representative of target population
Participant Motivation:
- Clear strategy for engaging reference households
- Documentation of incentive structures
- Compliance monitoring tools

Test Duration

Minimum Duration: 3 days required
Test Period Selection:
- Must capture representative cooking patterns
- Documentation of test day determination
- Justification of period appropriateness
Timing Considerations:
- Seasonal cooking variations
- Cultural factors affecting cooking habits
- Local event calendars

Data Collection Frequency

Climate Variations:
- Account for spatial and temporal climate differences
- Track seasonal impacts on fuel consumption
External Factors:
- Monitor traditional cooking patterns
- Track occupational influences
- Document cultural events
Continuous Monitoring:
- Ongoing coverage is best practice
- Any gaps require detailed justification
- Regular data validation checks

Sample Size Requirements

Population Coverage:
- Full population testing preferred
- Sampling allowed with proper design
Sample Determination:
- Follow sampling design guidelines
- Statistical significance requirements
- Documentation of selection process

Quick Start

curl -X POST https://api.emerging.eco/v1/claims \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "type": "KPTClaim",
    "householdId": "did:ixo:household/123",
    "testGroup": "intervention",
    "testPeriod": {
      "startDate": "2024-02-01",
      "endDate": "2024-02-04",
      "durationDays": 3
    },
    "fuelMeasurements": {
      "total": 11.9,
      "dailyLogs": [3.2, 2.8, 3.0, 2.9]
    },
    "externalFactors": {
      "season": "dry",
      "culturalEvents": ["none"],
      "occupationalFactors": ["typical_workweek"]
    }
  }'

from emerging import Client, KPTDesign

# Configure test design
design = KPTDesign.create(
    population_size=1000,
    confidence_level=0.95,
    margin_error=0.05
)

# Get random household selection
selected_households = design.get_random_selection()

# Submit KPT data
client = Client('YOUR_API_KEY')
kpt = client.claims.create(
    type="KPTClaim",
    household_id="did:ixo:household/123",
    test_group="intervention",
    test_period={
        "start_date": "2024-02-01",
        "end_date": "2024-02-04",
        "duration_days": 3
    },
    fuel_measurements={
        "total": 11.9,
        "daily_logs": [3.2, 2.8, 3.0, 2.9]
    },
    external_factors={
        "season": "dry",
        "cultural_events": ["none"],
        "occupational_factors": ["typical_workweek"]
    }
)

Components

Household Registration

Data Collection

Record daily fuel consumption with minimum 3-day observation period

Verification

Validate KPT results against protocols and control group data

Credentials

Issue verifiable KPT credentials with emission reduction data

Required Parameters

typestringrequired

Must be "KPTClaim"

householdIdstringrequired

DID of the household being tested

fuelMeasurementsobjectrequired

Daily fuel consumption measurements (minimum 3 days)

testGroupstringrequired

Indicates whether household is in "reference" or "intervention" group

Optional Parameters

cookingSessionsarray

Details of individual cooking events

evidencearray

Photos, documentation links, and selection methodology evidence

externalFactorsobject

Documentation of climate and other variables affecting consumption

Validation Rules

Data Consistency

Daily measurements must span minimum 3-day period
Total must match sum of daily logs
Values must be positive numbers
Seasonal variations must be documented

Methodology Compliance

Random selection must be verified
Control group data must be present
Test timing must align between groups
External factors must be documented

Response Format

{
  "id": "kpt-123",
  "status": "verified",
  "credential": {
    "type": "KPTCredential",
    "issuer": "did:ixo:validator/456",
    "evidence": [
      "ipfs://QmevidenceformX12"
    ]
  }
}

Error Codes

400error

Invalid KPT data format

401error

Unauthorized request

409error

Conflicting measurement data

Integration Examples

Combining with IoT Data and Control Groups

# Get KPT baseline for both groups
reference_baseline = client.kpt.get_baseline("reference-group")
intervention_baseline = client.kpt.get_baseline("intervention-group")

# Compare with IoT readings
iot_data = client.devices.get_usage("device-456")
reduction = calculate_reduction(reference_baseline, intervention_baseline, iot_data)

# Validate seasonal factors
seasonal_impact = client.kpt.analyze_seasonal_variations(reduction)

Tip

Always ensure compliance with minimum 3-day testing period and proper control group implementation.

Next Steps

Protocol Guide

Detailed KPT measurement protocols and methodological requirements

Field Manual

Best practices for data collection and control group management

Verification Guide

Understanding the validation process and emission reduction quantification

Best Practices

Test Design

Document random selection process
Ensure simultaneous testing of groups
Validate group representativeness
Monitor participant engagement

Data Collection

Maintain minimum 3-day duration
Track external influencing factors
Document seasonal variations
Ensure continuous monitoring coverage

Quality Control

Validate measurement consistency
Cross-reference with other data sources
Monitor dropout rates
Track data completeness

Documentation

Record selection methodology
Document test period justification
Track external factors
Maintain compliance evidence