Overview

The client wanted to develop a customized AI model that integrates drone and IoT data to predict solar panel maintenance needs, offering real-time alerts and a user-friendly interface for effective maintenance planning and optimization.

Technical Stack

  • TypeScript
  • SQL
  • C++
  • .Net
  • Java
  • Industry

    Information Technology

  • region
  • Region

    United States

  • project-size
  • Project Size

    $38,000-$42,000

Challenges

  • Utilizing cleaning agencies and unskilled workers increases costs and extends downtime for solar panels over a broad area.
  • Due to the high cleaning expenses and prolonged maintenance procedures, there is a need for proactive identification of maintenance needs to allocate resources efficiently.

Technical Challenges

  • Collecting and aggregating data from drone feeds posed a significant hurdle.
  • Creating a data pipeline capable of handling real-time data for instant predictions was a key objective.
  • Efficiently performing inferences on multiple feeds simultaneously while minimizing GPU usage was a priority.

Solutions

  • Our development team proposed an interactive web portal with AI models, including maintenance prediction and analyzing solar panel dust, mineral deposition, and physical damage. The objective was to empower end clients to enhance the effectiveness of their plant maintenance.
  • These AI models were designed to continuously learn and adapt even after deployment, ensuring real-time adaptation to changing scenarios. The maintenance prediction model, which considered over 40 parameters, aimed to provide highly accurate predictions of panel maintenance needs within a 14-day timeframe.

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Measurable Benefits post implementation

By implementing AI models and a centralized web application, clients experienced several notable advantages:

  • The monthly cost of maintaining personnel around the clock was slashed from 25,000 INR to just 4,800 INR per plant, based on a 0.5 MW plant requiring three personnel for maintenance. It's important to note that as the MW capacity increases, the need for personnel also rises.
  • Accurate maintenance forecasting substantially reduced plant downtime, decreasing it from 1-3 days per month to a mere 9 hours of monthly production loss.
  • Analyzing footage from each individual panel led to a 60% reduction in the time it takes to identify defects.
  • no.-of-resources
  • No. of Developers

    08

  • time-frame
  • Time Frame

    June 2019- July 2021

Experience With Bacancy

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