Photovoltaic Power Probability Estimation Using Weather-Segmented Copulas
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Solution Overview
Problem
Traditional copula functions have limitations in accurately predicting photovoltaic power due to their inability to effectively model the spatial correlation of power generation data from distributed photovoltaic power stations, leading to low accuracy in power prediction.
Innovation Solution
An optimized copula function method is developed, which involves classifying weather types using a clustering algorithm, constructing copula function models based on cumulative distribution data, evaluating these models for accuracy, and using the optimal model for point prediction and conditional probability estimation to predict power generation from distributed photovoltaic stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional copula function is used to model spatial correlation of photovoltaic power data, then the model construction is simple and data requirement is small, but the prediction accuracy is low due to inability to fit power data well
Solution Approach 1:
The patent segments the photovoltaic power system into centralized and distributed power stations, and further segments weather conditions into different types (sunny, cloudy, rainy, etc.). This segmentation allows the model to capture spatial correlations within each segment while maintaining manageability. The copula function is applied separately to each segment rather than attempting to model all data uniformly, thereby improving prediction accuracy without excessive complexity.
Solution Approach 2:
The patent applies different copula functions (Gaussian copula, t-copula, Archimedean copula) to different weather types and spatial locations based on local characteristics. Instead of using a single uniform model, the selection of copula function parameters and types is adapted to local weather patterns and spatial correlation characteristics, enabling the model to fit local power data better and improve overall prediction accuracy.
2Reliability
If traditional copula function is used for photovoltaic power prediction, then the data processing is straightforward, but the spatial correlation of power generation system cannot be represented accurately
Solution Approach 1:
The patent introduces dynamic adaptation by selecting different copula function types and parameters based on weather conditions and spatial locations. The model dynamically adjusts its structure to match the underlying correlation patterns in different scenarios, rather than relying on a static, one-size-fits-all approach. This dynamic capability enables accurate representation of spatial correlations under varying operational conditions.
Solution Approach 2:
The patent changes key parameters of the copula function based on weather types and spatial characteristics. Specifically, the correlation parameters and distribution parameters are adjusted according to historical data from different weather conditions and locations. This parameter adaptation allows the model to accurately capture the varying spatial correlations in photovoltaic power generation systems without requiring a completely different model structure for each scenario.
Data Source
AI summary
The present disclosure discloses a probability estimation method for photovoltaic power based on an optimized copula function and a photovoltaic power system. According to the method, weather types are classified by a clustering method to obtain a plurality of weather types, clustering is carried out based on historical meteorological data, and a copula function model is constructed based on clustering results. Historical operation data and weather classification results are considered at the same time to make the obtained hybrid Copula function model have higher prediction accuracy.