Low-Cost Solarimetric Station Using Machine Learning
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Solution Overview
Problem
Traditional solarimetric stations are expensive, complex to install and maintain, and have high uncertainty in measurements, making them unsuitable for widespread solar resource prospecting, especially in commercial solar generation projects where accurate data is crucial for photovoltaic design and development.
Innovation Solution
A low-cost, compact solarimetric station using photodiode pyranometers, machine learning algorithms, and wireless communication for real-time data acquisition and processing, capable of estimating direct and diffuse irradiance components, and hemispheric photographs, with a photovoltaic power system for autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scientific-standard solarimetric stations are used, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent segments the solarimetric measurement function into two parts: a simple low-cost pyranometer for global irradiance measurement and a separate machine learning model for deriving direct and diffuse components. This segmentation allows the physical hardware to remain simple while achieving sophisticated measurement capabilities through computational methods.
Solution Approach 2:
The patent replaces complex mechanical tracking systems and specialized sensors with a stationary pyranometer combined with machine learning algorithms. The machine learning model substitutes for the need for expensive specialized sensors and mechanical trackers, achieving similar measurement capabilities through software-based processing of simple sensor data.
2Reliability
If traditional solarimetric stations are deployed, then measurement reliability is improved, but installation and operation time increase
Solution Approach 1:
The patent implements autonomous operation where the station automatically performs measurements, processes data through machine learning algorithms, and transmits results without requiring specialized personnel for operation or maintenance. The system self-calibrates and self-monitors, eliminating the need for expert intervention and enabling rapid deployment.
Solution Approach 2:
The patent replaces complex mechanical tracking systems with a stationary sensor platform controlled by software algorithms. This eliminates mechanical failure points and reduces maintenance requirements, allowing faster installation and more reliable autonomous operation without specialized personnel.
3Measurement precision
If advanced solarimetric equipment is purchased, then measurement precision is improved, but acquisition cost increases
Solution Approach 1:
The patent uses inexpensive, commercially available pyranometers instead of expensive scientific-grade instruments. While individual sensors are lower cost, the overall system achieves comparable measurement precision through machine learning processing, making the approach economically viable for large-scale solar resource assessment.
Solution Approach 2:
The patent substitutes expensive specialized sensors and mechanical tracking systems with a simple stationary pyranometer and machine learning algorithms. This computational approach replaces costly hardware with software processing, dramatically reducing acquisition costs while maintaining measurement precision for solar resource prospecting.
4Reliability
If specialized personnel are assigned for maintenance, then measurement reliability is improved, but operational complexity increases
Solution Approach 1:
The patent designs the station to be fully autonomous, performing self-diagnosis, self-calibration, and automatic data processing. The system includes built-in monitoring that detects and corrects operational issues without external intervention, eliminating the need for specialized maintenance personnel and simplifying operation to basic installation and data retrieval.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Reduces the cost and complexity of solar resource prospecting while improving measurement accuracy and reliability, enabling faster implementation of photovoltaic projects with reduced uncertainty in solar potential assessment.
Implementation Method 1
A low-cost, compact solarimetric station using photodiode pyranometers
Implementation Method 2
with a photovoltaic power system for autonomous operation
Data Source
AI summary
The present invention relates to alternative equipment for solar energy prospecting with a focus on low cost, low complexity in installation, operation and maintenance, and high reliability. A low-cost solarimetric station consists of compact equipment capable of providing global irradiance measurements and estimates for direct and diffuse components, as well as hemispheric photographs, with acceptable levels of uncertainty. The pyranometer periodically provides global irradiance information to the system, and the camera records photos of the sky. Using machine learning algorithms, and based on that information, the equipment provides estimates for direct and diffuse irradiance components. The equipment has other meteorological sensors, GPS, and wireless communication facilities. The equipment has an energy supply and management system consisting of a photovoltaic module, charge controller, and battery, which provide the energy necessary for the station to operate.