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    Date submitted
  • 09-Apr-2017

AI and monitoring

Abstract

Artificial Intelligence, performance monitoring and field service software for operators of distributed energy production and storage systems (e.g. solar and/or battery). Examples of specific solutions include detecting underperforming system components, soiling, tree growth and energy dispatch or storage.

Video

Original YouTube URL: Open

Introduction Video

Additional Questions

Who is your customer?

Owners and operators of distributed energy production and storage systems.

What problem does this idea/product solve or what market need does it serve?

Automated performance monitoring and system performance analytics. Improve the accuracy of detecting underperforming system components, soiling, tree growth and energy dispatch or storage.

What attributes will make this idea/product successful? Why do you believe that those features will create success?

Machine learning and AI will: - automates the process - becomes smarter and more efficient over time - has higher accuracy over human

Explain how you (your team) will execute to make this idea/product successful? What gives you (your team) an advantage over others already in the market or new to this market?

We developed Surnun's primary monitoring software, a full suite of internal monitoring applications. This included AI, device API integrations, deployment architecture, outside data collection and management, etc. However, there's much to be improved upon and the company has deprioritized development until recently. The product currently in operation at Sunrun doesn't have any AI operationalized and is a basic visualization of much of the monitoring data collected from sources. Much of it uses simple thresholds to trigger alerting though a more refined approach using AI has been demonstrated by academia in the PV space.