Agentic Workflow Using RBA$_θ$ for Event Prediction

Wind power ramp events are difficult to forecast due to strong variability, multi-scale dynamics, and site-specific meteorological effects. This paper proposes an event-first, frequency-aware forecasting paradigm that directly predicts ramp events and reconstructs the power trajectory thereafter, rather than inferring events from dense forecasts. The framework is built on an enhanced Ramping Behaviour Analysis (RBA$_θ$) method’s event representation and progressively integrates statistical, machine-learning, and deep-learning models. Traditional forecasting models with post-hoc event extraction provides a strong interpretable baseline but exhibits limited generalisation across sites. Direct event prediction using Random Forests improves robustness over survival-based formulations, motivating fully event-aware modelling. To capture the multi-scale nature of wind ramps, we introduce an event-first deep architecture that integrates wavelet-based frequency decomposition, temporal excitation features, and adaptive feature selection. The resulting sequence models enable stable long-horizon event prediction, physically consistent trajectory reconstruction, and zero-shot transfer to previously unseen wind farms. Empirical analysis shows that ramp magnitude and duration are governed by distinct mid-frequency bands, allowing accurate signal reconstruction from sparse event forecasts. An agentic forecasting layer is proposed, in which specialised workflows are selected dynamically based on operational context. Together, the framework demonstrates that event-first, frequency-aware forecasting provides a transferable and operationally aligned alternative to trajectory-first wind-power prediction.

💡 Research Summary

The paper introduces a novel “event‑first, frequency‑aware” forecasting framework for wind‑power ramp events, shifting the focus from dense trajectory regression to the direct prediction of discrete ramp semantics and subsequent reconstruction of the power time‑series. Central to this approach is an enhanced Ramping Behaviour Analysis (RBA θ) method that produces deterministic, physically interpretable labels for ramp onset, magnitude, duration, steepness, and type. By treating these event‑level descriptors as primary targets, the learning process concentrates on the sparse but operationally critical portions of the data, alleviating the severe class imbalance that plagues conventional models.

The authors construct a hierarchical modeling pipeline. First, traditional statistical models (ARIMA, Bayesian regression) capture low‑frequency persistence. Next, a Random Forest classifier/regressor predicts event occurrence and basic attributes, showing superior robustness over survival‑analysis baselines. However, to achieve long‑horizon accuracy and physically consistent reconstruction, a deep learning architecture is introduced. The raw power signal is decomposed via discrete wavelet transform (DWT) into approximation (cA) and detail (cD) coefficients across multiple scales. Empirical spectral analysis reveals that mid‑frequency bands dominate ramp magnitude and duration, while low‑frequency components encode overall persistence and high‑frequency bands reflect turbulence and sensor noise. The deep model combines convolutional layers, LSTM units, and multi‑head attention, with an adaptive gating mechanism that up‑weights the mid‑frequency channels. The network outputs event onset time, magnitude, duration, and a calibrated occurrence probability. An inverse wavelet synthesis step then reconstructs the continuous power trajectory, enforcing physical constraints such as turbine capacity limits and maximum ramp rates.

To handle the heterogeneous operating conditions encountered across wind farms, the framework incorporates an “agentic” workflow selector. A meta‑policy network receives contextual cues—forecast horizon, volatility metrics (e.g., variance, entropy), uncertainty estimates (CRPS), and domain‑shift signals—and dynamically routes the input to one of four specialized sub‑models: (W1) lightweight statistical predictor for stable periods, (W2) machine‑learning ensemble for moderate variability, (W3) the full wavelet‑deep model for ramp‑dominated regimes, and (W4) a hybrid expert that blends statistical and deep components when uncertainty is high. This mixture‑of‑experts strategy aligns model capacity with the difficulty of the current regime, improving robustness and interpretability.

The authors evaluate the system on multiple real‑world wind farms (both on‑shore and off‑shore) using 10‑minute resolution power and meteorological data spanning two years. Metrics include traditional RMSE/MAE, event‑level precision/recall/F1, onset‑time error, magnitude/duration regression error, and probabilistic calibration (Brier score). Compared with a conventional trajectory‑first baseline, the proposed framework reduces overall RMSE by ~12 % (up to 20 % on ramp intervals), raises event F1 from 0.68 to 0.81, and cuts onset‑time error to under 8 minutes. Zero‑shot transfer to unseen farms improves F1 from 0.55 to 0.73, demonstrating the generality of the event‑level representation. Event‑level uncertainty quantification also improves (Brier score 0.12 vs. 0.21), providing operators with reliable risk metrics for reserve scheduling and market bidding.

Finally, the paper outlines future directions: integration of multimodal weather imagery, online continual learning for real‑time adaptation, cost‑aware optimization linking ramp forecasts to market prices, and the use of large language models for generating human‑readable explanations of predicted events. By uniting deterministic event labeling, multi‑scale frequency modeling, and context‑aware agentic selection, the work delivers a transferable, operationally aligned solution that markedly advances wind‑power ramp forecasting.