REAL-TIME SOLAR RADIATION MONITORING SYSTEM USING DEEP LEARNING AND IMAGE PROCESSING

Abstract

This invention introduces a solar radiation monitoring system that utilizes deep learning and image processing to estimate solar radiation levels in real time using satellite or sky images. The system leverages convolutional neural networks (CNN) to analyze atmospheric features, providing a scalable and accurate solution for wide-area solar radiation monitoring without the need for ground-based measurement devices. This system is particularly suitable for remote and underresourced regions, offering an efficient, cost-effective approach to solar radiation prediction. By integrating this monitoring system with solar energy plants and power grids, operators can dynamically adjust energy generation based on accurate radiation forecasts, improving grid management and reducing operational costs.

Specification

Field of Invention
This invention pertains to the renewable energy technology sector, specifically focusing on solar radiation monitoring systems that incorporate artificial intelligence and image processing techniques. The invention is relevant to the fields of energy management, environmental monitoring, and meteorology, where accurate solar radiation data is essential for operational efficiency.
Technical Field
The technical field of this invention is solar energy technology, with a particular emphasis on machine learning, deep learning, and computer vision applied to environmental data analysis. The system utilizes convolutional neural networks (CNN) to analyze satellite and sky imagery, providing precise solar radiation predictions that support sustainable energy management, resource optimization, and enhanced grid stability.
Background of the Invention
Traditional solar radiation measurement techniques rely on ground-based devices, such as pyranometers and other meteorological instruments, which are limited in spatial coverage, require regular calibration, and are challenging to deploy in remote areas. As the use of solar energy continues to expand, accurate and timely solar radiation data becomes crucial for solar energy generation, grid management, and efficient energy distribution.
Current methods for solar radiation estimation are often constrained by hardware limitations, high maintenance costs, and geographic accessibility. These
limitations hinder the adoption of solar energy, especially in remote or resourcelimited regions. Consequently, a need exists for a scalable and accurate solution to monitor solar radiation without relying solely on physical measurement stations.
This invention addresses these challenges by developing a solar radiation monitoring system that utilizes deep learning and image processing to estimate solar radiation based on satellite and sky images. By eliminating the dependency on ground-based measurement equipment, this system provides a cost-effective solution suitable for a wide range of applications in the solar energy sector.
Summary of the Invention
This invention presents a real-time solar radiation monitoring system that leverages deep learning and image processing for accurate radiation estimation. The system captures environmental images, which are processed through a convolutional neural network (CNN) to identify atmospheric features that influence solar radiation, such as cloud cover, sunlight intensity, and atmospheric clarity. The CNN model, trained on a comprehensive dataset of historical images and radiation measurements, provides highly accurate solar radiation estimates based on image data alone.
The system comprises three primary modules
> Image Acquisition Module
o This module collects images from various sources, such as satellites, ground-based cameras, or drones, providing continuous visual data on atmospheric conditions.
> Data Processing Module
o This module preprocesses images by resizing, normalizing, and filtering them to ensure high-quality input for the CNN model. Techniques such as brightness adjustment, contrast enhancement, and noise reduction are applied to improve image quality and standardize data.
> Analysis and Prediction Module
o Utilizing a CNN architecture, this module analyzes the preprocessed images to extract relevant atmospheric features. The CNN model, trained on historical solar radiation data, estimates current radiation levels based solely on visual data.
The system can be integrated with solar farms and energy grids, enabling operators to make real-time adjustments to energy generation and distribution based on radiation forecasts. This monitoring system is especially advantageous for remote or inaccessible areas, where traditional measurement devices are impractical or costly to deploy.
Detailed Description of the Invention
System Components
1. Image Acquisition Module
> The module gathers images at regular intervals from multiple sources to provide real-time data on atmospheric conditions.
> Sources include:
o Satellite-based imagery: Offers wide spatial coverage and allows monitoring over large regions.
o Ground-based sky cameras: Positioned at solar farms or other monitoring sites to provide high-resolution images.
> The images collected form the primary dataset for radiation analysis, capturing atmospheric features correlated with solar radiation.
2. Data Processing Module
> Preprocessing Steps
o Image resizing: Standardizes image dimensions for CNN input.
o Normalization: Scales image data to improve processing efficiency and model accuracy.
o Image enhancement: Techniques such as brightness adjustment, contrast enhancement, and noise reduction improve the quality of input images.
> Metadata Integration: Each image is tagged with metadata, such as timestamps and geographical coordinates, to enhance prediction accuracy.
3. Analysis and Prediction Module
> Convolutional Neural Network (CNN)
o The CNN model, trained on historical images paired with radiation measurements, extracts features such as cloud density, sky brightness, and sun position, which directly influence solar radiation levels.
o The model consists of several convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for radiation prediction.
> Model Training
o The CNN is trained using a dataset of labeled historical images and corresponding radiation data.
o Error metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are used to optimize the model's accuracy.
> Real-Time Prediction
o Once trained, the CNN model can predict solar radiation in real time based on new image data, enabling integration with energy systems for dynamic response.
System Integration
> Solar Energy Systems: The system can be integrated with solar farms to enable real-time monitoring of radiation levels, optimizing energy generation and reducing operational costs.
> Grid Management: Provides radiation predictions for grid operators to balance energy distribution, supporting grid stability by reducing dependency on reserve power sources.
> Remote Monitoring: Useful for monitoring radiation in inaccessible or underdeveloped areas, where traditional equipment is not feasible.
Description of Potential Applications
> Renewable Energy Generation: Enables solar farms to optimize energy output by dynamically adjusting operations based on solar radiation forecasts.
> Grid Management: Provides accurate solar radiation data for balancing energy loads, reducing reliance on reserve capacity.
> Remote Monitoring: Offers solar radiation data for remote areas, supporting renewable energy projects where physical devices are impractical.
Conclusion
The proposed solar radiation monitoring system provides a scalable, accurate, and efficient solution for monitoring solar radiation using deep learning and image processing. By minimizing reliance on ground-based devices, this invention reduces costs, enhances accuracy, and supports global renewable energy initiatives. This system can be used for accurate radiation forecasting in diverse environments, particularly in remote areas, promoting sustainable energy solutions.. . .

CLAIMS
Claim 1: A solar radiation monitoring system comprising:
> An image acquisition module for periodic atmospheric image capture;
> A data processing module to preprocess said images through normalization and enhancement;
> An analysis and prediction module that utilizes a convolutional neural network (CNN) to estimate solar radiation.
Claim 2: The system of claim 1, wherein the image acquisition module includes _ images from satellite-based, ground-based, or drone-based sources.
Claim 3: The system of claim 1, wherein the CNN model is trained using historical image data and corresponding radiation measurements, optimizing for Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
Claim 4: The system of claim 1, further comprising an integration module for realtime adjustments in solar energy systems based on radiation predictions.
Claim 5: The system of claim 1, wherein the CNN model identifies cloud cover, sun position, and atmospheric clarity from images.
Claim 6: The system of claim 1, providing real-time updates at intervals consistent with satellite or sky image availability.

Documents