Globally, non-technical loss (NTL) due to theft is a growing financial concern for energy utilities, costing an estimated $80 to $100 billion globally each year. Beyond the economic impact, theft threatens grid reliability by manipulating local area supply, thereby causing transformer overloading that can result in blackouts, damage to utility assets, poor customer experience and safety vulnerabilities.
Fortunately, AI-powered analytics applied to smart meter data can give utilities a much more precise and effective theft detection strategy. Rather than pursue theft only at the transformer, feeder, or substation levels (or the occasional meter tamper alert), modern theft analytics can reveal home-by-home consumption patterns consistent with partial or complete meter bypassing, meter tampering, tariff misuse and more.
Bidgely has developed this Energy Theft Detection playbook to help utilities leverage AMI data to more successfully mitigate theft-related and other NTL losses.
Step 1: Ingest and Analyze Data
Step 2: Accurately Detect Instances of NTL
Step 3: Strategically Target Bad Actors
