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Water — District

Greenvale Water District (synthetic)
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Group on Customer class Geography Service type Month Quarter Year
UnitTrack · Disclosure-controlled report

Water — District

Greenvale Water District (synthetic)
Generated 2026-07-27 19:30 · Grouped by customer class · k = 15
Residual re-identification risk
worst case 1% · Low · smallest published group 191
1/3
Customer class groups published
—
of meter reads in published output
2
groups withheld by disclosure control
15/15 rule
≥15 contributors; no single customer over 15%
Contents
  1. Disclosure control & data provenance
  2. Consumption profile
  3. Weather & seasonality
  4. Differential privacy (alternative release)
  5. Appendix — policy & citations

Disclosure control & data provenance

Grouping the dataset into 3 customer class groups, 1 were safe to publish and 2 were withheld under the rules below. The exact share published and the grand total are withheld here so the small suppressed group can't be recovered by subtraction; the published output carries a worst-case re-identification risk of 1% (Low).

Data quality & reliability
2250
Loaded reads
0 rejected & quarantined
4.6%
Estimated reads
utility estimate flag
0.2%
Negative usage
rollovers / net metering
334
High outliers
Tukey 1.5·IQR (1977)

Anomalies are flagged, not dropped; rows failing validation are quarantined as rejected. 9 reads (0.4%) lack coordinates and are excluded from spatial views.

Methods & authorities
  • Minimum-count (k-anonymity) — 1 group withheld (triggered by your minimum group size k = 15).
    Sweeney, k-anonymity (2002); ESSnet/Eurostat SDC Handbook (Hundepool et al.) — minimum-frequency / threshold rule · Meets/exceeds: CA CPUC D.14-05-016 ("15/15"); IL 220 ILCS 33
  • (n,k)-dominance — 1 group withheld (triggered by your top-1 dominance limit p = 15%).
    ESSnet/Eurostat SDC Handbook §4.2 (Hundepool et al.) — sensitive cells in magnitude tables
  • 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 (CCF) Contributors Status
INDUSTRIAL * — Withheld
COMMERCIAL * — Withheld
RESIDENTIAL 87,148 191 Published
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Consumption profile

Total consumption by customer class — published groups (CCF)
Consumption distribution & inequality
0.966
Gini coefficient
Gini 1912; Lorenz 1905
9.471
Coefficient of variation
std ÷ mean
338
Median use / account (CCF/yr)
across 250 accounts
41
High outliers
Tukey 1.5·IQR
Lorenz curve — cumulative use vs. cumulative accounts
Percentiles (CCF/yr)
10th155
25th226
Median338
75th623
90th2195
Customer class × month
Total consumption — class (rows) × month (columns); blanks were suppressed (CCF)
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Weather & seasonality

19.7%
Weather-sensitive (heating-driven)
ASHRAE G14; PRISM
100.0% / 0.0%
Heating / cooling split
of weather-driven use
—
Weather-normalized annual
restated at normal year
0.105
Model fit (R²)
degree-day regression

E = 1769 + 0.9966·HDD  ·  base 65°F · 9 months

Outdoor / irrigation use — 59.4% above winter baseline, peak Feb
Average monthly consumption vs. winter baseline (CCF)
Winter-baseline (minimum-month) method. EPA WaterSense; AWWA; Water Research Foundation.
Explore weather live →

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.
Explore differential privacy live →

Appendix — policy & citations

Disclosure-control settings
Governing standard15/15 rule
Minimum group size (k)15
Dominance (top-n ≤ p)1 ≤ 0.15
Min accounts for models15
GroupingCustomer 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 19:30.