PROJECT OBJECTIVES

Digital detailed Real Time Mapping that relates to in-situ condition

Digital Address Mapping and digital identity

Customer Identification and Demographics Data 

Predictive and Performance Analytics

DATA COLLECTION

Customer Enumeration/Mapping and Collation of the following Customer data:

Demographics (Name of Landlord/Tenant, Number of Occupants, Gender, Number of Adults, Teens, Children)

Load assessment (Type and number of Energy consuming equipment)

Pictures and Videos of Houses

Videos of identified areas with anomalies

Customer Status (Metrics to evaluate Customers’ status e.g Number of Cars, size of building e.t.c)

Line Route Survey (Wiring Tracing)

TECHNOLOGICAL INNOVATION

The Project involved Tools for Automation and real time Visibility in the following areas:

Digital Mapping of Magodo Area

Use of Body CAMs and GPS tracking

Automated Data Entry by Field Staff using iPads

Database software for collation

Data Mining and Extraction

PREDICTIVE PERFORMANCE AND ANALYSES

Customer Analytics

Vending Pattern

Consumption Forecast for Power Allocation Planning

Exception Analysis:

Potential Theft Identification

Tariff Mismatch

Meter Re-certification

Operational Efficiency Analytics

Metering Density

Billing Efficiency

Collection Efficiency

ATC&C loss analysis

SYSTEMS AND TOOLS

100-inch Hisense TV for real time display of customer data, GIS Operations Dashboard and Digital Maps in the Control Room

ArcGIS Survey 123 app for Digital Data Capturing of customer information

ArcGIS Field Maps app for Line Route Survey and Validation of DTs on the field

40 Samsung Galaxy S7 plus tablets and accessories (Screen Guards and Protective pouches), installed with the ArcGIS Survey 123 app and ArcGIS Field Maps app.

40 Sim Cards for Internet data to enable field operations

SQL Sever Cloud Database created for collation and storage of captured customer data from the field

SQL Server Management Studio (SSMS) for database administration and data extraction from the database. 

Python and SQL Server Management Studio (SMSS) to build algorithms for Customer clustering and analytics

RESULTS

On an average, only 38% of the DTs under the Magodo Bilateral power scheme, have ATC&C loss below 10%, leaving the remaining 62% with ATC&C above 10%.

These losses are majorly commercial losses which result from improper Energy accounting, meter bypass/energy theft.

Further Analysis of the DTs with ATC&C loss above 10% show that over 68% of the customer meters under these DTs are located inside their buildings and supports the theory of meter bypass in vulnerable locations.

Prompt meter relocations and outright meter replacement of old meters would reduce the risk of meter bypass and improve the revenue recovery performance of Magodo Bilateral Location.