Wind Power Forecasting With DVINE Copulas for Spatial Dependency
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Solution Overview
Problem
Traditional probabilistic forecasting of wind power output faces challenges due to nonstationary and nonlinear temporal dynamics, boundedness, high observation frequency, and asymmetric spatial dependency, which affect the accuracy and efficiency of wind power integration into power supply systems.
Innovation Solution
A spatio-temporal probabilistic forecasting method using a DVINE copula model to analyze residuals from temporal models, combined with support vector regression hybridization, to accurately predict wind power output across multiple wind farms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional probabilistic forecasting methods are used for wind power output, then the forecasting process is simple, but the accuracy is reduced due to nonstationary and nonlinear temporal dynamics
Solution Approach 1:
The forecasting system segments the complex wind power prediction problem into multiple temporal models (ARIMAX, dynamic autoregressive, quantile autoregressive) that can be fitted to different aspects of the data. Each model captures specific temporal patterns, and their predictions are combined to achieve high accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system uses a composite modeling approach by combining multiple temporal models with a DVINE copula spatial model. This composite structure integrates different modeling techniques (autoregressive, moving average, copula functions) to handle the nonstationary and nonlinear characteristics of wind power data, achieving superior forecasting accuracy.
2Measurement precision
If multiple temporal models are fitted to capture nonstationary dynamics, then forecasting accuracy improves, but computational complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-fitting multiple temporal models to historical data and pre-computing their predictions. This allows the system to have accurate forecasts ready in advance, reducing the need for intensive real-time computation and improving overall computational efficiency while maintaining high accuracy.
Solution Approach 2:
The system applies partial action by selectively using multiple temporal models only when needed for capturing complex patterns, rather than always employing all models. This selective approach balances computational effort with accuracy requirements, avoiding excessive computation when simpler models suffice.
3Measurement precision
If a DVINE copula model is used for spatial dependency, then spatial correlation accuracy improves, but model fitting complexity increases
Solution Approach 1:
The DVINE copula model serves as an intermediary that connects the predictions from multiple temporal models, capturing the spatial dependency between wind farms. It mediates the relationship between individual farm predictions and regional patterns, achieving accurate spatial correlation while keeping the overall system manageable through this specialized connecting component.
4Adaptability or versatility
If wind power data is normalized by installed capacity, then comparability across wind farms improves, but information about absolute power output is lost
Solution Approach 1:
The system applies parameter changes by normalizing wind power data using the ν-logit transformation, which converts the bounded power output data into an unbounded scale. This transformation preserves information while enabling comparability across different wind farms with varying installed capacities, and the inverse transformation can recover absolute power values when needed.
Data Source
AI summary
A method for forecasting power output of a target site. The method includes normalizing power output data for the target site, at least in part, on an installed capacity. The normalized power output data is transformed to yield transformed normalized power output data. A temporal module fits a temporal model to model input data for the target site. The model input data corresponds to normalized power output data or transformed normalized power output data. A copula model is fit for the target site, based, at least in part, on at least one residual value. Each residual value is determined based, at least in part on a selected fitted temporal model for each target site.