Insolation Probability Distribution Analysis for Solar Prediction
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Solution Overview
Problem
Precise prediction of solar power generation requires simplification of insolation data processing and numerical modeling of insolation probability distribution, considering multiple meteorological and electronic factors, which is challenging with existing methods.
Innovation Solution
An insolation probability distribution analysis method using a processor to determine a model with Gaussian probability density functions, allowing for efficient mathematical modeling and calculation of insolation data, and simplifying statistical processing by expressing data as a mixed Gaussian distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If insolation data is processed using detailed statistical methods to achieve precise solar power generation prediction, then prediction accuracy is improved, but processing complexity and calculation time increase
Solution Approach 1:
The patent transforms insolation data into a probability distribution model with specific parameters (mean, standard deviation, skewness, kurtosis) that capture the essential statistical characteristics. This parameter transformation allows precise prediction while simplifying the representation of complex insolation variations, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent replaces complex mechanical/statistical processing methods with a standardized probability distribution framework. By substituting detailed statistical analysis with a parametric probability model, the system achieves comparable or superior prediction accuracy while significantly reducing computational complexity and processing requirements.
2Measurement precision
If multiple meteorological and electronic factors are considered in solar power generation calculation, then prediction precision is improved, but calculation complexity increases
Solution Approach 1:
The patent merges multiple meteorological and electronic factors into a unified probability distribution model of insolation. By combining these diverse factors into a single statistical framework characterized by parameters like mean, standard deviation, and higher-order moments, the system maintains comprehensive consideration of all factors while simplifying the overall calculation structure.
Solution Approach 2:
The patent transforms multiple influencing factors into a set of standardized probability distribution parameters. This parameter transformation allows the system to account for the combined effects of various meteorological and electronic factors without requiring separate calculations for each factor, thereby reducing calculation complexity while preserving prediction precision.
3Measurement precision
If insolation data is stored and processed in detailed form, then data accuracy is maintained, but database load and processing time increase
Solution Approach 1:
The patent extracts the essential statistical characteristics from detailed insolation data and stores only the key parameters (mean, standard deviation, skewness, kurtosis) of the probability distribution. This extraction process removes redundant detailed information while preserving the critical statistical properties needed for accurate solar power generation prediction, thereby reducing database load and improving processing efficiency.
Solution Approach 2:
The patent creates a simplified probabilistic representation (a copy) of the detailed insolation data that captures its essential statistical behavior. This copied probability distribution model requires minimal storage space compared to the original detailed data while enabling efficient processing and maintaining the accuracy needed for solar power generation calculations.
Data Source
AI summary
Statistical processing of insolation data for calculation prediction which requires calculation with conditional branching using an insolation as a variable, or analyze a histogram of an insolation probability distribution based on a probabilistic analysis and mathematically model the insolation as a calculation formula. The techniques described herein can be used to help predict the solar power generation by a solar power generation system.