Enhance mobile customer experience using Machine Learning

AGL is an energy generator and retailer servicing over 3.4m customers and employing 3,700+ people. In 2018 AGL generated $12.8b revenue

Business challenge

  • AGL relies on accurate readings of customers’ electricity and gas meters in order to produce invoices
  • Outside of Victoria, the vast majority of meters are analog which need to be manually read
  • If the meter cannot be accessed energy usage is estimated, which can lead to bill shock
  • In response, AGL allows customers to read their own meter and provide the estimate, however getting an accurate reading is challenging without guidance

Success critera

  • Evaluate the feasibility of using machine learning to assist customers in reading their own meter
  • Develop a custom machine vision solution to classify different types of energy meters and accurately extract readings
  • Deploy the solution within the AGL mobile app, ensuring it can function with limited or no mobile signal coverage and without requiring connectivity to AGL systems

Solution

  • AGL provided Eliiza with a large set of images for training purposes that were representative of the breadth and complexity of the use case
    • Several different meter types each with a different layout
    • Meter layout not intuitive and not clear how to infer a reading
    • Meter often one of many, for example in an apartment block, and not clear how a customer can identify which is theirs
    • Images often impacted by glare or reflection which impacted accuracy of the reading
  • A joint Eliiza and AGL team was assembled to accurately label each image to enable model training
  • Using the live video stream on the mobile device enabled the solution to perform up to 10 reads per second, building up a more accurate prediction, and providing real time feedback to the user guiding them through identifying their meter and performing an accurate reading
  • Eliiza selected Tensorflow on Google Cloud’s AI Platform to train the model using accelerated computing (GPU/TPU)
  • The model was deployed within the iOS and Android apps using Tensorflow lite to optimise for the mobile device
https://eliiza.com.au/wp-content/themes/salient/css/fonts/svg/basic_elaboration_smartphone_picture.svg

AGL customer points mobile camera at their energy meter

https://eliiza.com.au/wp-content/themes/salient/css/fonts/svg/basic_eye.svg

App determines the meter type and takes reading

https://eliiza.com.au/wp-content/themes/salient/css/fonts/svg/basic_elaboration_cloud_refresh.svg

The reading is passed to AGL for processing

https://eliiza.com.au/wp-content/themes/salient/css/fonts/svg/basic_elaboration_document_check.svg

Customer invoice is updated accordingly

Outcomes

  • The solution was developed over a period of 12 weeks and tested via field trials in South Australia 
  • Over 300 trial meter readings were performed: ~74% achieved accuracy of within $5 of the actual bill 
  • Average time to take readings was 5–6 seconds, less than half the time taken by an average human reader
  • The model architecture and pipeline was handed over to AGL for ongoing development and production release
  • For a more detailed synopsis, see our blog post

Contact Us

We’re always keen to start conversations on how AI can help solve real-world problems, or opportunities for technology to impact people in a positive way.

Contact Eliiza