Automated Shading Analysis

Automated Shading Analysis is an advanced solar design process that uses AI, computer vision, and 3D modeling to automatically detect and evaluate shading on rooftops, ground sites, or canopies. Instead of manually marking trees, buildings, vents, chimneys, parapets, or obstructions, automated shading tools scan the environment and calculate how shadows will move across the solar array throughout the year.

This automation provides accurate shading-loss estimates, optimizes module placement, and significantly reduces both design time and human error. Platforms like Solar Designing and shade engines such as Shadow Analysis rely heavily on automated shading for layout optimization, AC/DC sizing, and performance modeling.

Automated shading analysis is essential for any designer, EPC, or installer aiming to maximize energy yield, meet production guarantees, or generate accurate proposals.

Key Takeaways

Automated Shading Analysis uses AI and 3D modeling to instantly detect shading and calculate energy losses.
Significantly reduces manual work and improves design accuracy.
Essential for module placement, inverter sizing, and performance modeling.
Supports residential, commercial, and utility-scale designs.
Integrates with tools like Solar Designing and Shadow Analysis.

What Is Automated Shading Analysis?

Automated Shading Analysis is the process of evaluating solar access using algorithms that:

Detect shading objects
Map obstruction heights
Simulate sun paths
Calculate daily, monthly, and annual shading losses
Identify productive vs. non-productive zones
Optimize module placement based on irradiance

It replaces manual inspection with precise, data-driven modeling. Designers can instantly see which roof areas are viable and how shading will affect energy production.

How Automated Shading Analysis Works

Although different tools use different methods, the core workflow typically includes:

1. Surface Detection

AI identifies roof planes, edges, and orientation using satellite imagery, drone scans, or LiDAR.

2. Obstruction Recognition

Algorithms automatically detect:

Trees
Chimneys
Neighboring buildings
Parapets
Vents, pipes, HVAC units
Terrain features

3. Height Estimation

3D modeling reconstructs obstruction height relative to the module surface.

4. Sun Path Simulation

Calculates shading for each hour of the year based on:

Latitude
Seasonal position of the sun
Horizon profile

5. Irradiance Mapping

The system assigns irradiance values to each area of the roof using POA data.

6. Shading Loss Calculation

Results include:

Monthly shading losses
Annual shading percentage
Hourly irradiance values
Module-level shading hotspots

7. Auto-Optimizing Module Placement

Modules are automatically placed in the highest-yield areas—critical for DC/AC sizing and performance modeling.

See Solar Layout Optimization for how shading influences panel placement.

Types / Variants of Automated Shading Analysis

1. Satellite-Based Shading Analysis

Uses satellite imagery and elevation data. Suitable for residential and remote proposals.

2. LiDAR-Based Shading Analysis

Most accurate. Uses high-resolution 3D terrain and building models.

3. Drone-Based 3D Shading Analysis

Ideal for commercial and complex roofs.

4. AI Obstruction Detection

Machine learning identifies trees and objects automatically from imagery.

5. Real-Time Shading Simulations

Used when combined with Automated Shading Analysis tools.

6. Module-Level Shading Mapping

Heavily used in MLPE-based systems.

How It's Measured

Automated shading tools generally measure shading in:

Solar Access (%)

Percentage of annual sunlight available.

Shading Loss (%)

Annual energy lost due to shade.

POA Irradiance (W/m²)

The amount of sunlight hitting the panel surface.

Heat Map Visualization

Shows high-shade vs. high-production zones.

Hourly Irradiance Curves

Used for energy modeling and inverter sizing.

Typical Values / Ranges

Ground-mount or utility-scale systems often optimize row spacing to reduce inter-row shading.

Practical Guidance for Solar Designers & Installers

1. Run shading analysis early in the workflow

Detect shading problems before layout and stringing.

2. Always evaluate winter shading

Sun is lowest in winter—most shading occurs here.

3. Combine shading with Auto-Layout

Tools like Solar Designing automatically avoid low-production zones.

4. Use accurate input data

LiDAR or drone models significantly improve accuracy.

5. Pay attention to horizon shading

Tall objects far away still affect morning and evening production.

6. Avoid placing modules near chimneys and tall vents

Even small obstructions can cause major module mismatch losses.

7. Validate automated shading results

Compare against on-site photos or drone scans if available.

8. Use shading results in proposals

Shading maps increase customer confidence in projected performance.

Real-World Examples

1. Residential Rooftop

Automated shading identifies that two roof sections lose 18–25% due to tree shading.

Auto-design places modules on the south-facing plane only, raising system yield.

2. Commercial Flat Roof

HVAC units cast long shadows during early morning and late afternoon.

Automated shading avoids these zones and reserves pathways for O&M access.

3. Ground-Mount Solar Farm

Terrain shading detection prevents module placement near slopes.

Row spacing is automatically optimized to reduce inter-row shading losses.