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Imply > Case Studies > AI-Driven Analytics Revolutionizing the Power Industry: A Case Study on Innowatts
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AI-Driven Analytics Revolutionizing the Power Industry: A Case Study on Innowatts

Technology Category
  • Other - Battery
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Electrical Grids
  • Utilities
Applicable Functions
  • Product Research & Development
Use Cases
  • Microgrid
  • Time Sensitive Networking
The Challenge
Innowatts, an AI-enabled SaaS platform, was facing the challenge of managing and analyzing data from 40 million meters worldwide to provide near-real-time energy analytics and actionable business intelligence to utilities and retailers. The company needed to aggregate meter level data to create reports and recommendations for customers. The goal was to help energy providers be more predictive, proactive, and connected, unlocking grid edge opportunities, increasing customer value, and accelerating the transition to sustainable energy solutions. The challenge was not only to manage the massive data sets but also to provide insights and recommendations based on usage patterns, such as suggesting better electricity plans or products like electric vehicle battery storage.
About The Customer
Innowatts' customers are utilities and retailers from around the world who rely on the company's AI-enabled SaaS platform for near-real-time energy analytics and actionable business intelligence. These customers depend on Innowatts for insights that help them be more predictive, proactive, and connected. They look to Innowatts to unlock grid edge opportunities, increase customer value, and accelerate the transition to sustainable energy solutions. The customers also benefit from Innowatts' ability to recommend different products for them to offer their customers, based on the insights derived from the analysis of meter data.
The Solution
Innowatts leveraged Apache Druid, an open-source data store designed for real-time queries, to aggregate meter level data and create reports and recommendations for customers. The company used Druid to quickly work with massive data sets and differentiate its platform with the ability to read meters and forecast usage on the fly. Innowatts' team runs regular batch ingestion on energy market data, which alleviates the overhead of operating an Apache Kafka cluster 24/7. Data arrives right before the market opens and the Innowatts platform creates a forecast immediately. The team scales their Druid cluster up and down every day, increasing the data servers on the cluster to speed up ingestion and then scaling down when not needed. The team queries Druid via Plywood, a query tool created by Imply, because data is returned in a format that is easy to be fed into D3.js for visualization.
Operational Impact
  • The use of Apache Druid has expanded far beyond its intended use case at Innowatts, leading to expanded insights that clients gain through the SaaS platform. The ability to quickly work with massive data sets and forecast usage on the fly has differentiated Innowatts' platform. The company has been able to provide BI innovation and sub-second querying to its clients, especially during the COVID-19 pandemic when there was a shift in electricity consumption from commercial to the residential side. Customers have expressed satisfaction with the speed of Innowatts' front end, which is driven by sub-second query response times in Druid. The company has built an industry-leading SaaS platform for the energy sector.
Quantitative Benefit
  • Innowatts helped its customers see a 40% improvement in forecast accuracy within 3 months.
  • Enhanced customer lifetime value by $3,000 per customer.
  • Avoided $4 million in Opex costs.

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