NET2GRID Drives Transition to Green Energy, Builds Utility Monitoring Solution on AWS
Dutch home energy management company NET2GRID built machine learning models on AWS to disaggregate household energy consumption by appliance with over 90% accuracy, and edge models that identify appliance events with 98% accuracy, while predicting next-day energy consumption for utilities with 98% accuracy.
Overview
Dutch home energy management company NET2GRID built machine learning models on AWS to disaggregate household energy consumption by appliance with over 90% accuracy, and edge models that identify appliance events with 98% accuracy, while predicting next-day energy consumption for utilities with 98% accuracy.
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Inspect the highlighted sourceThe challenge
NET2GRID's energy-monitoring solution needed to scale after its customer base grew 10x in under two years, but its cloud-agnostic architecture using containers, relational databases and EC2 instances was not cost-effective or scalable enough for a mass-market business case.
The solution
NET2GRID rebuilt its platform as a serverless architecture on AWS using AWS Lambda, moved from relational databases to flat storage on Amazon S3 queried via Amazon Athena, and used Amazon Data Firehose to stream smart-meter data, layering its own machine learning algorithms (general appliance-recognition models, per-household disaggregation models, and pretrained edge models) on top of Utility Meter Data Analytics on AWS.
Reported business value
NET2GRID cut AWS service costs by 400-500%, scaled to support 10x user growth, achieved over 90% accuracy in real-time appliance-level energy disaggregation, 98% accuracy identifying appliance events within 15 seconds, and 98% accuracy predicting next-day energy consumption (worth millions to utilities and up to 3x ROI for customers), while reaching an 85%+ customer referral rate and four-star app ratings.
Sources
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