Applying Image Segmentation in Photovoltaic Power Forecasting

Based on Patent Research | CN-116565829-A (2024)

Grid operators struggle to predict solar energy output because changing weather causes frequent power fluctuations. These errors lead to inefficient resource use and high backup costs. Image segmentation helps by identifying specific cloud types and their density from sky camera photos. This computer vision process separates pixels into distinct categories to measure cloud cover precisely. Using these detailed maps allows utilities to improve generation forecasts and maintain more stable electricity distribution for their customers.

SOLUTION OVERVIEW

Manual Forecasting Automated via Segmentation

Image segmentation serves as a sophisticated tool for managing grid stability by analyzing environmental data with pixel-level precision. This technology begins by receiving real-time feeds from ground-based sky cameras or satellite sensors. It then processes these visuals by classifying every individual pixel into specific categories like cirrus clouds, thick cumulus formations, or clear sky. By delineating the exact boundaries of these atmospheric features, the system calculates precise density maps that determine how much light reaches solar panels at any given moment.

Automating this visual analysis allows utilities to integrate high-speed weather data directly into their energy management systems. Just as a driver uses a high-definition navigation system to anticipate traffic jams before they appear, grid operators can use these digital cloud maps to foresee generation drops before they occur. This predictive capability reduces the need for expensive, idle backup plants and ensures smoother power distribution across the network. Reliable computer vision integration ultimately fosters a more resilient and efficient infrastructure for renewable energy adoption.

HOW IT WORKS

Capturing Power Forecasts from Imagery

Capturing High Resolution Atmospheric Data

The system begins by gathering real-time visual feeds from ground-based sky cameras and satellite sensors installed across the utility network. These high-definition images provide the raw environmental data necessary to monitor changing weather patterns and solar irradiance levels. By establishing a constant stream of visual information, the platform creates a foundation for monitoring the precise conditions affecting solar arrays.

Identifying Pixel Level Cloud Characteristics

Advanced computer vision algorithms analyze every individual pixel in the captured visuals to distinguish between clear sky and various cloud formations. This segmentation process classifies features such as thin cirrus clouds or thick cumulus layers by recognizing their unique visual signatures and densities. The result is a highly detailed digital map that defines the exact boundaries of atmospheric interference.

Calculating Real Time Solar Irradiance

By processing the classified pixel maps, the system determines how much sunlight is reaching the solar panels at any specific location. These calculations convert visual cloud density data into actionable metrics regarding potential energy generation drops. This stage transforms complex imagery into precise measurements that help grid operators understand the immediate impact of weather on power production.

Optimizing Grid Distribution and Stability

The final processed data is integrated into energy management systems to provide accurate short term forecasts of power output. These insights allow utilities to balance the grid effectively by adjusting backup resources before fluctuations occur, reducing operational costs. This predictive capability ensures a more resilient infrastructure that can reliably support the widespread adoption of renewable energy sources.

Potential Benefits

Enhanced Power Grid Stability

Precise pixel-level cloud mapping allows grid operators to anticipate solar fluctuations before they impact the network. This foresight helps maintain a steady electricity supply by balancing energy loads more effectively.

Lower Operational Backup Costs

By accurately predicting sunlight availability, utilities can reduce their reliance on expensive and idle backup power plants. This optimization of resources leads to significant cost savings in energy generation and management.

Improved Renewable Energy Integration

Automated image segmentation identifies different cloud densities to refine solar output forecasts. High-quality data integration supports a more resilient infrastructure and accelerates the adoption of clean energy sources.

Real-Time Precise Resource Management

Transforming camera feeds into detailed atmospheric maps provides instant visibility into changing weather conditions. This digital capability enables utilities to automate high-speed adjustments for more efficient power distribution.

Implementation

1 Install Field Sensors. Deploy ground-based sky cameras and satellite receivers across the grid to capture high-definition atmospheric data.

2 Calibrate Segmentation Models. Configure the computer vision algorithms to accurately classify pixels into categories like cloud type and density.

3 Map Solar Irradiance. Establish the conversion protocols that transform processed visual data into real-time solar energy generation metrics.

4 Integrate Management Systems. Connect the automated cloud mapping outputs directly into existing utility energy management and distribution software.

5 Automate Grid Forecasting. Enable the predictive analytics layer to adjust backup resource allocation based on anticipated solar output fluctuations.

Source: Analysis based on Patent CN-116565829-A "Short-term photovoltaic power generation capacity prediction method based on SOM-BP neural network" (Filed: August 2024).