Detecting and Classifying Rooftop Using Neural Networks to Eliminate Energy Waste

Background

Energy companies and sustainability-focused organizations face challenges in accessing reliable customer electricity data, which is essential for identifying efficiency and sustainability opportunities. The lack of this data slows operational decision-making and go-to-market strategies for solutions like solar energy installations, retrofits, and energy consulting.

A Techstars Energy startup sought a digital mapping solution to provide energy assessments for 250,000 facilities across North America, including insights into their kilowatt-hour consumption. One critical indicator of energy consumption is the type and characteristics of facility rooftops.

Objective

The project aimed to develop an AI-powered solution for classifying rooftop types and characteristics to enhance energy consumption understanding. This would support predictive analytics for energy efficiency and sustainability initiatives, enabling faster decision-making for energy sales teams and broader societal impact.

Approach

To address the challenge, Omdena gathered a team of 50 AI changemakers to develop a cutting-edge AI model over two months. The approach involved:

1. Data Collection: Access to rooftop images from over 200,000 facilities in North America.
2. Model Development: A convolutional neural network (CNN) and edge detection techniques were employed to classify rooftops into five distinct categories:
   • Flat and Clear
   • Flat and Cluttered (e.g., rooftops with HVAC equipment)
   • Light Industrial
   • Heavy Industrial
   • Existing Solar (rooftops with solar panels)
3. Training and Testing: The partner provided labeled image samples to train the CNN. Edge detection was explored to distinguish complex industrial rooftops from simpler ones.
4. Technology: Tools included Python-based machine learning frameworks, GIS mapping software, and predictive analytics platforms.

Results and Impact

The project successfully classified rooftop types, providing actionable insights into facility energy intensity. Key outcomes include:

• A scalable AI model capable of analyzing rooftop characteristics for over 200,000 facilities, with plans for global expansion to Canada, Europe, and Asia.
• Enhanced predictive analytics for energy sales teams, accelerating decision-making and improving market targeting.
• Support for energy efficiency and sustainability efforts, reducing time-to-impact for retrofits, solar installations, and other solutions.
• Increased understanding of rooftop complexity, paving the way for additional facility classifications.

This solution strengthens the digital mapping platform, empowering energy companies to quickly identify high-impact facilities, optimize operations, and contribute to a sustainable future.

Future Implications

This AI-driven rooftop detection model has broader applications beyond energy efficiency. Future research could:

• Expand the classification categories to encompass more facility types.
• Integrate rooftop characteristics with real-time energy consumption data for deeper insights.
• Influence policy by demonstrating the importance of accessible electricity data for sustainable development.
• Support global adoption, providing scalable solutions for regions with emerging energy markets.

The project exemplifies how AI can drive transformative impact in sustainability and energy efficiency.