Revenue Protector
Revenue Protector helps utilities identify non-technical losses, abnormal consumption and water leaks through continuous metering data analysis. Using machine learning and anomaly scoring, it prioritises suspicious cases and supports energy balance calculations. These features help teams detect issues earlier, reduce manual checks and plan field inspections more accurately.
Overview
Designed to complement Energy Data Manager, Revenue Protector is built on the same cloud-native platform and leverages trusted metering data without duplication. It supports electricity and water utilities with advanced use cases like loss and leak detection as well as energy balance.
Features
Machine learning analyses meter behaviour and network conditions to identify abnormal consumption patterns. Loss scores compare measured consumption with expected values based on voltage data and local network behaviour, helping utilities identify and prioritise potential non-technical losses.
Suspicious cases are prioritised through anomaly scores, helping teams focus investigations on the most relevant meters. The analysis can be performed on historical consumption data and applied across large meter populations to identify areas and individual meters with potentially higher relative losses.
Uses virtual data sources to compare energy delivered and consumed across the distribution network, supporting energy balance calculations. This helps utilities identify where losses occur and detect potential issues at an early stage.
Validates which smart meters are connected to each feeder or transformer, improving accuracy for solutions like Grid Optimizer and Revenue Protector.
Continuously analyses water consumption patterns to identify and localise potential leaks on the customer or network side. It provides visibility into where and when abnormal consumption occurs and how it evolves over time across the meter population.
Supports action directly from the dashboard, allowing users to drill down to individual meters, trigger workflows and initiate work orders. This helps streamline investigation and response to abnormal events while reducing manual steps
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