Wind Power Forecasting Framework
The global transition toward carbon neutrality demands scalable and reliable renewable energy solutions, with wind power playing a central role in this transformation. However, the inherent volatility and complexity of wind data present a formidable challenge for accurate and stable forecasting. Traditional models have often prioritized predictive accuracy while neglecting the critical need for stability, leading to unreliable grid-level decision-making. This research addresses that gap by proposing a robust, data-driven point-interval prediction framework. It blends innovative decomposition methods, model integration strategies, and a novel multi-objective optimization mechanism to deliver both precision and resilience in forecasts. The approach demonstrates significant improvements over conventional techniques, suggesting a paradigm shift in wind power forecasting methodology.
2. Addressing Forecast Stability in Wind Power Predictions
While most predictive models in renewable energy emphasize accuracy, stability is equally crucial for operational reliability—especially in power systems where erratic forecasts can jeopardize grid stability. This study's novel framework emphasizes stability by introducing a two-tiered fusion strategy. In point prediction, the integration module balances multiple model outputs through a weight-based fusion mechanism, effectively mitigating volatility in results. In interval prediction, optimization of the bandwidth parameter further enhances stability by reducing overfitting and under-coverage. The dual focus on accuracy and stability ensures that the model remains practical for real-world grid operations, offering reliable data to inform energy dispatching and reserve allocation.
3. Adaptive Data Decomposition Using Sliding Window Techniques
To combat the erratic nature of wind power data, the proposed framework leverages a unique sliding window-based two-layer adaptive decomposition strategy. Unlike conventional decomposition methods that risk information leakage or irregular segment generation, this technique segments data into structured, meaningful subsequences while preserving time-series integrity. This preprocessing step not only improves the effectiveness of downstream forecasting models but also ensures that temporal dependencies are accurately maintained. This approach significantly enhances the interpretability and granularity of predictions, laying the groundwork for better model deployment and real-time adaptability in renewable energy systems.
4. Complexity-Based Subsequence Classification for Model Efficiency
A key innovation of the research lies in reducing computational redundancy through Lempel-Ziv complexity-based classification. By analyzing the information complexity of decomposed subsequences, the model intelligently prioritizes resources, assigning simpler models to low-complexity sequences and more powerful architectures to high-complexity ones. This tailored deployment enhances the computational efficiency without compromising on performance, enabling faster and more scalable forecasting pipelines. This classification strategy represents a meaningful shift toward resource-aware AI applications in power systems, improving both prediction speed and scalability for real-time applications.
5. Multi-Model Integration with Advanced Deep Learning Architectures
The research incorporates cutting-edge forecasting models—iTransformer, TimesNet, Mamba2, and SCINet—within a single integrated architecture. Each model brings unique strengths in capturing non-linearities, temporal dependencies, and cross-scale patterns. Their strategic integration through a two-stage fusion module maximizes their individual benefits while minimizing weaknesses. This ensemble approach enables the system to adapt dynamically to varying patterns in the data, producing more reliable outputs. By intelligently leveraging the diversity of models, the framework enhances its robustness against outlier effects and sudden changes in wind behavior.
6. Empirical Validation and Impact on Grid Operation Strategy
The performance of the proposed framework is validated through rigorous empirical analysis using two real-world wind power datasets. Statistically significant reductions in MAE and STD—up to 58.51% and 77.72%, respectively—highlight the model's superior accuracy and stability. These improvements directly translate into more reliable forecasting for grid operators, allowing for better uncertainty quantification and proactive grid management. As a result, the framework not only advances academic modeling techniques but also contributes to the practical enhancement of renewable energy integration into power systems, marking a key step toward smarter and more resilient grids.
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