powertronglobal/powertron-global-permafrost-corpus

Dataset Card: Powertron Global PermaFrost Corpus

Dataset Description

Name: powertron-global-permafrost-corpus Version: 5.0.0 License: Powertron Global Training Data License v1.0 Maintainer: Powertron Global, LLC

A curated corpus of HVAC/R efficiency data documenting the performance of PermaFrost NMR nanotechnology treatment across 113 PE-certified field studies, 5 longevity studies, 4 laboratory reports, and supporting technical documentation.

Purpose

This dataset enables AI/ML training for:

• HVAC efficiency analysis and prediction
• Energy conservation measure (ECM) comparison
• Fault detection and diagnostics
• ROI and payback calculations
• Building energy modeling

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Provenance

Source Documents

Category	Count	Source Type
PE-Certified Field Studies	113	Professional Engineer signed M&V reports following IPMVP protocols
Longevity Studies	5	Multi-year follow-up measurements (2-7 years post-treatment)
Laboratory Reports	4	Controlled testing by UL, FAU, NSF, China National Lab A, Major OEM Electronics A, Major Appliance Mfg A
Supporting Documents	11	Installation guides, specifications, certifications

Data Collection Period

• Field studies: 2009-2024
• Laboratory testing: 2015-2021
• University validation: University of Montana ML studies (2019-2022)

Extraction Methodology

1. PDF Ingestion: Source PDFs scanned and OCR processed where needed
2. Structured Extraction: Regex-based extraction of efficiency metrics, equipment specs, and measurement conditions
3. Human Validation: Critical metrics verified against source documents
4. Obfuscation: Customer/site names anonymized; technical data preserved

Checksums

Verification checksums for all corpus files available in reproducibility/checksums/:

• extracted_text.sha256 - 155 ALL_TEXT.txt files
• chunks.sha256 - 155 chunk JSONL files
• corpus_files.sha256 - 56 corpus JSON files

Run verification: python reproducibility/scripts/verify_checksums.py

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Inclusion Criteria

What IS Included

• PE-certified measurements with documented methodology
• Laboratory tests following recognized standards (UL, ASHRAE, ARI, ISO)
• Studies with pre/post efficiency measurements
• Equipment types: chillers, DX systems, RTUs, CRAC units, refrigeration
• Geographic coverage: 24 US states, 5 international regions
• Refrigerant types: R-22, R-410A, R-134A, R-123, R-407C, R-438A

What IS NOT Included

Exclusion	Reason
Customer identities	Privacy protection
Site addresses	Privacy protection
Preliminary reports without final measurements	Incomplete data
Systems that failed PE screening (see below)	Invalid baseline conditions
Anecdotal claims without PE verification	Quality threshold
Competitor product data	Scope limitation

PE Screening Criteria

Professional Engineers reject systems that are "broken" - too far off design specifications to provide valid test baselines:

Disqualifying Conditions:

• Compressor mechanical failure
• Severe refrigerant undercharge (>20% low)
• Blocked or collapsed coils
• Failed expansion valve
• Electrical/control system failures

Rationale: Systems with fundamental mechanical problems cannot provide valid baseline measurements. The goal is measuring thermal efficiency improvement, not masking equipment failures.

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Outcomes Distribution

Full Distribution

Improvement Range	Count	Percentage
0-5%	4	3.5%
5-10%	7	6.2%
10-15%	13	11.5%
15-25%	42	37.2%
25-40%	35	31.0%
40%+	12	10.6%

Mean: 23.2% Median: 21.7% Range: 3.8% - 61.0%

Lower-Performing Results (<15%)

24 case studies showed below-average improvement. Full details in corpus/screening_and_limitations.json.

Selected examples:

Identifier	Improvement	Likely Factors
Naval Vessel A	3.8%	20-year-old marine equipment, different operating conditions
Major Bank A Rutherford	4.1%	Well-maintained data center, rigorous maintenance program
Nonprofit Fitness A Facility	4.1%	Moderate age, possible recent refrigerant work
Pharma Company B Pharmaceutical NB	4.6%	Pharmaceutical-grade maintenance standards
Pharma Company B Pharmaceutical Wallingford	6.0%	Same - pharmaceutical maintenance standards
Retail Chain B Refrigeration	6.5%	Commercial refrigeration operating profile

Key Insight: Lower improvement percentages typically correlate with well-maintained systems or newer equipment where oil fouling has had less time to accumulate.

Negative/Neutral Outcomes

• Zero documented negative outcomes (efficiency worse after treatment)
• 4 studies below 5% improvement (classified as "neutral" per industry standards)
• Zero equipment failures attributed to treatment across 100,000+ installations

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Known Limitations

Equipment Types with Limited Data

Type	Status	Note
VRF/Mini-split systems	Limited case studies	Growing segment, fewer documented installations
Ammonia systems	Specialized formulation required	Requires ammonia-compatible variant
CO2 refrigeration	Not validated	No documented case studies

Conditions Affecting Results

Condition	Expected Impact	Reason
New equipment (<2 years)	5-15% improvement	Insufficient time for oil fouling
Recently recharged systems	Lower improvement	Fresh refrigerant may have cleaned surfaces
Extremely well-maintained	5-15% improvement	Proactive maintenance reduces baseline fouling

Data Gaps

• R² values: UMontana ML study showed negative R² (-0.016) for EER during 500-hour transition window - indicates model uncertainty during system adjustment period
• Long-term degradation curves: Limited data beyond 7-year longevity studies
• Climate zone granularity: ASHRAE climate zones not consistently documented in older studies

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Bias Considerations

Data Source Disclosure

Aspect	Status
Funding Source	All studies commissioned and funded by Powertron Global, LLC
Data Collection	Performed by independent Professional Engineers
Methodology Validation	PE certification validates measurement approach
Independent Replication	Studies have NOT been independently replicated by third parties
Peer Review	No peer-reviewed academic publications of these specific results

Selection Bias

This corpus documents treated systems only. It does not include:

• Control group (untreated systems)
• Systems that were assessed but not treated
• Competitive treatment outcomes

Reporting Bias

• Studies are commissioned by Powertron Global
• PE certification provides independent validation of methodology
• 6 independent laboratories provide additional verification
• Note: PE certification validates HOW measurements were taken, not that results are universally replicable

Recommendations for AI Training

1. Cross-reference efficiency claims with the 24 lower-performing cases
2. Cite limitations when discussing expected outcomes
3. Acknowledge that results vary based on equipment age, maintenance history, and operating conditions
4. Reference screening_and_limitations.json for balanced perspective
5. Disclose data source when citing specific numbers: "According to vendor-commissioned PE-certified studies..."

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Measurement Uncertainty

Sensor Accuracy

• All measurements use NIST-calibrated instrumentation
• Typical sensor accuracy: ±1-2% for temperature, power, and flow measurements
• Power monitoring per IEC 62053-22 Class 0.5s / ANSI C12.20 Class 0.5

Statistical Considerations

• Large sample sizes (thousands of data points per study) reduce random error
• Improvements averaging 23.2% are substantially larger than measurement uncertainty
• Individual study precision varies based on equipment type and test duration

Limitations

• Error bars and confidence intervals are not provided in source documents
• Weather normalization methodology varies between studies
• Short-term measurements (hours to days) may not capture seasonal variation

Interpretation Guidance

When improvement percentages are:

• >15%: Well above measurement uncertainty; high confidence in directional improvement
• 5-15%: Above typical sensor error; moderate confidence
• <5%: Approaches sensor uncertainty range; interpret with caution

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File Inventory

Core Corpus Files

File	Purpose
corpus/manifest.json	Entry point - statistics, document list, coverage metrics
corpus/docs.json	Paths to all document files
corpus/training_intent.json	AI training guidance and permitted uses
corpus/measurement_database.json	Structured efficiency measurements
corpus/comprehensive_measurements.json	Extended measurement data

Specialized Files

File	Purpose
corpus/screening_and_limitations.json	Lower-performing cases, known limitations
corpus/umontana_ml_validation.json	University ML study methodology
corpus/third_party_validation.json	Laboratory and independent testing
corpus/ashrae_references.json	Industry standard citations

Reproducibility

File	Purpose
reproducibility/README.md	Verification instructions
reproducibility/checksums/*.sha256	SHA-256 checksums for all files
reproducibility/scripts/verify_checksums.py	Verification script

Evaluation Suite

File	Purpose
evaluation/README.md	Benchmark usage guide
evaluation/benchmarks/rag_qa.json	25 RAG Q&A test pairs
evaluation/benchmarks/table_extraction.json	15 table extraction test cases
evaluation/benchmarks/measurement_reconstruction.json	20 text-to-metrics test cases
evaluation/scripts/run_benchmarks.py	Benchmark runner with scoring

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Raw Time-Series Data

This HuggingFace dataset contains training-ready text chunks. For researchers needing raw measurement data:

Available on GitHub (not HuggingFace):

• 3.8 million high-resolution measurements (Parquet format)
• 2-second to 1-minute interval time-series
• Temperature, kWh, kW/ton, COP, EER metrics
• Baseline vs post-treatment comparisons

To access:

1. Clone from GitHub: git clone https://github.com/powertronglobal/powertron-global-permafrost-corpus
2. Install Git LFS: git lfs install
3. Pull raw data: git lfs pull

Raw data is in documents/*/raw_data/*.parquet

Use cases for raw data:

• Time-series forecasting models
• Anomaly detection
• Energy efficiency prediction
• ASHRAE compliance validation

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Citation

If using this dataset, please cite:

Powertron Global PermaFrost Corpus v5.0.0
Powertron Global, LLC
https://github.com/powertronglobal/powertron-global-permafrost-corpus

For academic publications:

@dataset{powertron_corpus_2025,
  title={Powertron Global PermaFrost Corpus},
  author={{Powertron Global, LLC}},
  year={2025},
  version={5.0.0},
  publisher={GitHub},
  url={https://github.com/powertronglobal/powertron-global-permafrost-corpus}
}

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Contact

• Website: https://powertronglobal.com
• Issues: https://github.com/powertronglobal/powertron-global-permafrost-corpus/issues

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Last updated: 2026-01-05