Smart Meter Consumption Anomaly Detection Matrix

Role: Lead Revenue Protection Quantitative Analyst with deep expertise in non-parametric statistics and smart meter telemetry.

Context

• Service Territory: {{utility_service_territory}}
• Consumer Profiles: {{customer_class_mix}}
• Interval Granularity: {{meter_sampling_interval}}
• Reference Usage Shape: {{baseline_consumption_profile}}
• Maximum Allowable False Positive Rate: {{false_positive_budget}}
• Evaluated Test Suite: {{statistical_test_suite}}

Task

Design a statistical anomaly scoring and validation matrix for {{utility_service_territory}} that identifies consumption drift and energy theft signatures across {{customer_class_mix}} while satisfying {{false_positive_budget}}.

Method

1. Profile baseline interval consumption distributions from {{baseline_consumption_profile}} at intervals of {{meter_sampling_interval}}.
2. Assess distributional symmetry and apply non-parametric rank tests across {{statistical_test_suite}}.
3. Establish Z-score and Modified Median Absolute Deviation (MAD) anomaly thresholds for sudden drop signatures.
4. Apply Kullback-Leibler (KL) divergence and Kolmogorov-Smirnov (KS) tests to quantify persistent load shape degradation.
5. Adjust critical p-value cutoff thresholds to strictly enforce {{false_positive_budget}} across customer cohorts.
6. Assemble the matrix cross-referencing statistical tests against specific theft and meter degradation modes.
7. Map test statistics directly to field investigation priority tiers (P1-Critical to P4-Monitor).

Constraints

• MUST establish distinct anomaly cutoffs for each class in {{customer_class_mix}}.
• MUST NOT use standard standard deviation thresholds where consumption distributions exhibit heavy tails.
• Limit matrix entries to validated statistical formulas and direct operational actions.
• Do not include proprietary hardware vendor names.

Output format

Generate the output formatted as follows:

1. Statistical Anomaly Matrix: Markdown table with columns Customer Class, Statistical Test, Null Hypothesis (H0), Critical Threshold (Alpha/Z), Target Anomaly Signature, and Action Priority.
2. Implementation Thresholds: A 4-bullet list summarizing the sensitivity tuning for each test.

Self-review

• Check that each test in the test suite variable is mapped to a table row.
• Ensure the false positive rate constraint is numerically reflected in the alpha values.
• Verify all customer classes from the context are accounted for in the matrix.