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Electric — Municipal Utility

Greenfield Municipal Power (synthetic)

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Group on Customer class Geography Service type Month Quarter Year

UnitTrack · Disclosure-controlled report

Electric — Municipal Utility

Greenfield Municipal Power (synthetic)

Generated 2026-07-27 18:45 · Grouped by customer class · k = 15

Residual re-identification risk

worst case 3% · Low · smallest published group 37

3/3

Customer class groups published

100.0%

of meter reads in published output

groups withheld by disclosure control

15/15 rule

≥15 contributors; no single customer over 15%

Contents

Disclosure control & data provenance
Consumption profile
Geography & spatial clustering
Weather & seasonality
Demand & load
Benchmarking (EUI)
Energy burden & equity
Measurement & verification (M&V)
Differential privacy (alternative release)
Appendix — policy & citations

Disclosure control & data provenance

Grouping the dataset into 3 customer class groups, 3 were safe to publish and 0 were withheld under the rules below. Across 18000 meter reads, 100.0% of records and 100.0% of total total consumption remain in the published output, with worst-case re-identification risk of 3% (Low).

Data quality & reliability

18000

Loaded reads

0 rejected & quarantined

4.0%

Estimated reads

utility estimate flag

0.3%

Negative usage

rollovers / net metering

2763

High outliers

Tukey 1.5·IQR (1977)

Anomalies are flagged, not dropped; rows failing validation are quarantined as rejected. 156 reads (0.9%) lack coordinates and are excluded from spatial views.

Methods & authorities

No groups required suppression at these settings (k = 15).
PII handling — identifiers are excluded from the analytic path; only disclosure-controlled aggregates are reported.
NIST SP 800-122; NISTIR 8053

Published results

Customer class	Total consumption (kWh)	Contributors	Status
INDUSTRIAL	54,388,597	37	Published
COMMERCIAL	39,401,813	233	Published
RESIDENTIAL	27,296,524	1230	Published

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Consumption profile

Total consumption by customer class — published groups (kWh)

Consumption distribution & inequality

0.739

Gini coefficient

Gini 1912; Lorenz 1905

3.708

Coefficient of variation

std ÷ mean

21721

Median use / account (kWh/yr)

across 1500 accounts

230

High outliers

Tukey 1.5·IQR

Lorenz curve — cumulative use vs. cumulative accounts

Percentiles (kWh/yr)

10th	10315
25th	14478
Median	21721
75th	39206
90th	140491

Customer class × month

Total consumption — class (rows) × month (columns); blanks were suppressed (kWh)

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Geography & spatial clustering

Random

Spatial pattern

not significant

0.147

Moran's I

expected -0.125

0.65

z-score

over 9 areas

0.5168

p-value

two-tailed

Total consumption by area (kWh) — uncolored areas are suppressed

No significant spatial pattern — values look randomly placed.

1 hot spot (Getis-Ord Gi*, p < 0.05).

Global Moran's I, rook contiguity; normality-assumption inference. Moran (1950); Cliff & Ord (1973). Basemap © OpenStreetMap/Carto.

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Weather & seasonality

7.3%

Weather-sensitive (cooling-driven)

ASHRAE G14; PRISM

0.0% / 100.0%

Heating / cooling split

of weather-driven use

—

Weather-normalized annual

restated at normal year

0.952

Model fit (R²)

degree-day regression

E = 7039 + -4.2528·HDD + 2.4549·CDD · base 65°F · 11 months

Seasonal use — 37.6% above winter baseline, peak Aug

Average monthly consumption vs. winter baseline (kWh)

Winter-baseline (minimum-month) method. Standard seasonal decomposition.

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Demand & load

0.284

Median load factor

across 1500 buildings

4921

Coincident peak (kW)

hour 10:00

0.133

Coincidence factor

NREL UMP Ch.10

7.53

Diversity factor

1 ÷ coincidence

Peak demand by hour of day (summed customer peaks; tallest = system coincident peak)

Load factor by class

Class	Buildings	p25	Median	p75
RESIDENTIAL	1230	0.233	0.271	0.307
COMMERCIAL	233	0.368	0.405	0.447
INDUSTRIAL	37	0.48	0.516	0.579

Load factor: utility load research; NREL UMP. Coincidence/diversity: NREL UMP Ch.10; BPA M&V. Small classes withheld (k-anonymity).

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Benchmarking — Energy Use Intensity

35.7

Median EUI (kBtu/ft²/yr)

EPA ENERGY STAR; DOE/EIA CBECS

1500

Buildings benchmarked

floor area & ≥ 2 months

1 kWh = 3.412 kBtu; 1 therm = 100 kBtu (EIA)

reference constants v2024.1

EUI distribution across buildings (published classes)

By customer class (site EUI, kBtu/ft²/yr)

Class	Bldgs	p25	Median	p75	vs nat'l
RESIDENTIAL	1230	23.4	36.4	57.4	121%
COMMERCIAL	233	15.5	29.2	57.4	42%
INDUSTRIAL	37	19.2	53.7	131.2	—

Within-dataset EUI percentiles vs. class peers. “vs nat'l” is coarse all-fuel context (CBECS 2018 / RECS 2020), not the ENERGY STAR 1–100 score. Small classes withheld.

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Energy burden & equity

Burden = annual energy cost ÷ tract median household income at $0.16/kWh. Bands: ≤6% affordable · 6–10% moderate · >10% high (ACEEE).

4.69%

Median tract burden

across 46 tracts

High-burden tracts

> 10% of income

23.9%

Households high-burden

300 of 1253

Tracts suppressed

below min-count

Energy burden by census tract (% of income)

Energy burden = annual energy cost ÷ median household income; ≤6% / 6–10% / >10% cut points (6% affordability from the Home Energy Affordability Gap [Fisher, Sheehan & Colton]; used by DOE/NREL LEAD & ACEEE — convention, not statute). Income: ACS 5-year B19013. Rate: EIA average retail price. Tract-level per DOE/NREL LEAD; metered-commodity burden is a lower bound on total home-energy burden.

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Measurement & verification (IPMVP Option C)

Baseline model fit on 5 months before Jul 2023; savings measured over the following 6 reporting months. Synthetic demo with no intervention — avoided energy near 0% is the method working correctly.

-1.3%

Avoided energy

-549 kWh/meter

41891

Adjusted baseline (CBL)

kWh/meter expected

42440

Reporting actual

kWh/meter metered

0.953

Baseline fit (R²)

degree-day model

Reporting period: metered actual vs. adjusted baseline (kWh/meter)

IPMVP Option C (EVO 10000-1:2016); ASHRAE Guideline 14; base-65°F degree days (US EIA / NOAA).

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Differential privacy — an alternative release

An alternative to suppression: instead of hiding small groups, calibrated Laplace noise is added to each count (ε = 1.0, scale b = 1.0) so no individual's presence can be inferred while totals stay useful. Mean absolute error ±0.7 across 3 groups.

True vs. differentially private counts by customer class

Laplace mechanism, ε-differential privacy. Dwork, McSherry, Nissim & Smith (2006); Dwork & Roth (2014); US Census Bureau 2020 DAS.

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Appendix — policy & citations

Disclosure-control settings

Governing standard	15/15 rule
Minimum group size (k)	15
Dominance (top-n ≤ p)	1 ≤ 0.15
Min accounts for models	15
Grouping	Customer class

Standards & sources cited

k-anonymity — Sweeney (2002); ESSnet/Eurostat SDC Handbook
(n,k)-dominance — ESSnet SDC Handbook §4.2; CPUC 15/15
Inequality — Gini (1912); Lorenz (1905)
Spatial — Moran (1950); Cliff & Ord (1973)
Weather / M&V — ASHRAE Guideline 14; IPMVP Option C; PRISM
Benchmarking — EPA ENERGY STAR; DOE/EIA CBECS/RECS
Load — NREL UMP; BPA M&V Peak Demand
Burden — ACEEE; US Census ACS
Differential privacy — Dwork et al. (2006); US Census 2020 DAS
PII — NIST SP 800-122; NISTIR 8053; NIST SP 800-188
Methods & Formulas — every rule & analysis with its formula-as-implemented and primary citation

Synthetic sample data — for demonstration, not distribution. Generated by UnitTrack, a Polish Clover Solutions product · 2026-07-27 18:45.