Wind Power Forecasting With DVINE Copulas and Temporal Models
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Solution Overview
Problem
Existing wind power forecasting technologies face challenges in accurately predicting wind power output due to nonstationary and nonlinear temporal dynamics, bounded data, high observation frequency, and asymmetric spatial dependency across multiple wind farms.
Innovation Solution
A spatio-temporal probabilistic forecasting method that normalizes wind power output data based on installed capacity, transforms the data using a u-logit transformation, fits temporal models such as Naïve-ARIMAX and Transformed ARIMAX, and employs a DVINE copula model to capture spatial dependencies, with model parameters adjusted using support vector regression hybridization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional probabilistic forecasting methods are used for wind power data, then the forecasting process is simple, but the accuracy is insufficient due to nonstationary and nonlinear temporal dynamics
Solution Approach 1:
The forecasting system is segmented into multiple specialized modules: a preprocessor module for data normalization, a temporal module for time-series modeling with multiple candidate models (ARIMAX, dynamic autoregressive, quantile autoregressive), and a spatial module for copula-based spatial dependency modeling. Each module handles specific aspects of the complex wind power forecasting problem, allowing high accuracy through specialized processing while managing overall system complexity through modular architecture.
Solution Approach 2:
The system employs composite modeling approaches by combining multiple temporal models (ARIMAX, dynamic autoregressive, quantile autoregressive) with spatial copula models. This composite structure integrates different modeling techniques to capture both temporal dynamics and spatial dependencies, achieving superior forecasting accuracy that neither model type could achieve alone.
2Adaptability or versatility
If multiple wind farms are considered for spatial forecasting, then comprehensive coverage is improved, but asymmetric spatial dependency becomes more difficult to model
Solution Approach 1:
The spatial module implements local quality by allowing different copula models to be selected for different pairs of wind farms based on their specific spatial relationships and dependency characteristics. Rather than applying a uniform spatial model across all wind farms, the system adapts the spatial dependency modeling to local conditions, capturing asymmetric spatial dependencies more effectively while maintaining versatility across multiple locations.
3Measurement precision
If wind power output data is normalized based on installed capacity, then comparability across wind farms is improved, but information loss may occur
Solution Approach 1:
The preprocessor module acts as an intermediary by introducing normalized wind power output as an intermediate representation that preserves comparability across wind farms. The normalization based on installed capacity serves as a mediator that enables meaningful comparison while the system subsequently processes both normalized and transformed data through multiple modeling stages, minimizing information loss by maintaining multiple data representations throughout the forecasting pipeline.
Data Source
AI summary
A method for forecasting wind power output of a target wind farm. The method includes normalizing, wind power output data for each wind farm of a group of wind farms, based, at least in part, on a respective installed capacity; transforming, the normalized power output data to yield transformed normalized wind power output data. Fitting, by the temporal module, each temporal model of at least one temporal model to model input data for each wind farm. The model input data corresponds to normalized wind power output data or transformed normalized wind power output data. The method further includes fitting, by a spatial module, a DVINE copula model for the group of wind farms, 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 wind farm in the group.