Wind Power Interval Forecasting with Chance-Constrained Learning
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Solution Overview
Problem
Traditional wind power prediction methods face challenges in accurately predicting wind power intervals due to their reliance on deterministic point predictions and fixed quantile proportions, leading to conservative interval widths and increased operational costs, especially when dealing with asymmetric probability distributions.
Innovation Solution
A chance constrained extreme learning machine method that generates wind power prediction intervals without pre-specifying quantile proportions, minimizing interval width while ensuring a nominal confidence level, adaptable to symmetric or asymmetric distributions, and applicable to other renewable energy sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deterministic point prediction methods are used, then the prediction output is simple and easy to obtain, but the prediction accuracy is insufficient and cannot quantify uncertainty
Solution Approach 1:
The patent transforms the prediction output from a single deterministic value to an interval with multiple parameters (lower bound, upper bound, confidence level). This allows the model to quantify uncertainty while maintaining computational efficiency through the extreme learning machine framework.
Solution Approach 2:
The prediction interval boundaries are made adaptive rather than fixed. The model dynamically adjusts the interval width and quantile proportions based on the learned distribution characteristics, allowing the prediction uncertainty to vary with input conditions while maintaining computational tractability.
2Measurement precision
If prediction intervals are constructed with fixed symmetrical quantile proportions, then the method is simple to implement, but the interval width becomes conservative and increases operational costs
Solution Approach 1:
The patent explicitly allows and utilizes asymmetric quantile proportions for the prediction interval boundaries. Instead of forcing symmetry around the median, the model learns optimal asymmetric quantiles that better fit the wind power distribution, reducing unnecessary conservatism while maintaining computational simplicity.
Solution Approach 2:
The quantile proportions are changed from fixed predetermined values to adaptive parameters learned from data. This allows the interval construction to adapt to the actual distribution characteristics of wind power, achieving tighter intervals without increasing operational complexity.
3Ease of operation
If the prediction interval width is minimized, then the operational cost is reduced, but the confidence level requirement may not be met
Solution Approach 1:
The patent incorporates a confidence level constraint that provides feedback on whether the prediction interval meets the required reliability. The optimization process adjusts the interval width based on this feedback, ensuring the nominal confidence level is satisfied while minimizing the interval width to reduce operational costs.
Solution Approach 2:
The confidence level is treated as a configurable parameter rather than a fixed requirement. By allowing the confidence level to be adjusted, the system can find the optimal balance between interval width (operational cost) and reliability, adapting to different operational scenarios.
4Adaptability or versatility
If parametric hypotheses are imposed on wind power probability distribution, then the model structure is simplified, but the model cannot adapt to time-varying distribution characteristics
Solution Approach 1:
The patent employs a nonparametric approach where the model structure itself adapts to the data distribution without requiring explicit parametric assumptions. The extreme learning machine automatically learns the underlying distribution characteristics from the data, making the model self-adaptive to time-varying conditions while maintaining computational efficiency.
Solution Approach 2:
The model structure is made dynamic to accommodate changing distribution characteristics. Rather than assuming a fixed parametric form, the model adapts its parameters and structure based on the input data, allowing it to capture time-varying distribution features without requiring complex reconfiguration.
Data Source
AI summary
The present application discloses a chance constrained extreme learning machine method for nonparametric interval forecasting of wind power, which belongs to the field of renewable energy generation forecasting. The method combines an extreme learning machine with a chance constrained optimization model, ensures that the interval coverage probability is no less than the confidence level by chance constraint, and takes minimizing the interval width as the training objective. The method avoids relying on the probability distribution hypothesis or limiting the interval boundary quantile level, so as to directly construct prediction intervals with well reliability and sharpness. The present application also proposes a bisection search algorithm based on difference of convex functions optimization to achieve efficient training for the chance constrained extreme learning machine.