RapidValue (Aspire Systems) > Case Studies > Remote Monitoring & Predictive Maintenance App for a Solar Energy System

Remote Monitoring & Predictive Maintenance App for a Solar Energy System

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 Remote Monitoring & Predictive Maintenance App for a Solar Energy System - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Renewable Energy
Applicable Functions
  • Sales & Marketing
Use Cases
  • Predictive Maintenance
About The Customer
A leading manufacturer and distributor of solar panels and solar energy based out of the U.S. They provide complete and fully integrated solar system, including modules, trackers, inverters, ISIS energy management software, networking, AC & DC disconn
The Challenge

The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process. 

The Solution

- Integration with inverters to collect data. - Detection of possible malfunctioning with predictive analytics. - Mobile & Cloud-based solutions with automated real-time alert notifications for field engineers. - HR scheduling solution to gather real-time details of available filed engineers & provide location information for faster issue resolution. - Integration with onsite cameras for live feeds and document management for product manuals and suggested resolution.

Data Collected
Control System Alert, Fault Detection, Solar Power
Operational Impact
  • [Efficiency Improvement - Energy]
    Achieved maximum utilization of solar energy through remote controlling of solar panel directions.
  • [Efficiency Improvement - Maintenance]
    Reduced time spent in monitoring locations and downtime through predictive maintenance.
  • [Efficiency Improvement - Operation]
    Improved efficiency through process automation
Quantitative Benefit
  • Reduced operational cost by 25-30%

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