Solar Installation Proposal Modeling with Stereoscopic Imaging
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Solution Overview
Problem
Existing methods for generating solar energy system proposals are time-consuming, inaccurate, and reliant on human error, particularly in rural areas with limited data availability, and often require multiple iterations due to the need for human intervention in designing and updating proposals.
Innovation Solution
An automated system using stereoscopic imaging and neural networks to generate highly accurate solar production models and interactive proposals, eliminating the need for LiDAR instruments and human intervention, allowing real-time proposal generation and seamless education of potential customers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated systems using stereoscopic imaging and neural networks are implemented, then measurement precision and productivity are improved, but device complexity increases
Solution Approach 1:
The patent replaces manual measurement methods and traditional LiDAR instruments with an automated system using stereoscopic imaging and neural networks. The system captures images with cameras and uses machine learning algorithms to automatically generate accurate solar production models, eliminating the need for complex mechanical measurement devices and human intervention.
Solution Approach 2:
The system creates digital copies of physical structures through stereoscopic imaging and point cloud generation. By capturing spatial information through multiple camera angles and generating 3D point cloud representations, the system produces accurate digital models that can be used for solar production analysis without physically measuring the site.
2Manufacturing precision
If multiple iterations with human intervention are required, then manufacturing precision may be maintained, but loss of time increases
Solution Approach 1:
The system performs self-service by automatically generating solar production models and proposals without requiring human intervention or multiple iterations. The neural network processes the captured images and point cloud data autonomously to produce accurate proposals, eliminating the time-consuming manual review and revision cycles that previously were necessary.
Solution Approach 2:
The system performs preliminary actions by pre-processing images and generating point cloud data that are then automatically converted into solar production models. This preliminary automated processing eliminates the need for subsequent manual iterations and revisions, achieving both speed and accuracy in proposal generation.
3Measurement precision
If LiDAR instruments are used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces expensive, complex LiDAR instruments with more affordable stereoscopic imaging systems using standard cameras. While LiDAR provides accurate measurements, the system demonstrates that cheaper camera-based stereoscopic imaging combined with neural network processing can achieve comparable accuracy for solar production modeling, reducing device complexity and cost.
Solution Approach 2:
The system substitutes mechanical LiDAR measurement systems with an optical-based stereoscopic imaging approach. Instead of using laser ranging and time-of-flight measurements, the system uses multiple camera angles and neural network algorithms to extract spatial information, achieving similar measurement precision with simpler, less expensive equipment.
Data Source
AI summary
Systems and methods are disclosed herein for designing and proposing an installation of a project for a structure or site that can be based on identifying installation surface faces, installation surface face benefit metrics, obstructions, obstacles affecting placement of elements of the project, and for providing an educational experience for an interested party to the project, comprising general information and at least one structure-specific installation proposal. A selected plan can be implemented from within a proposal platform of the disclosed systems and methods.