PaperlayerView exact revision on arXiv

Source-linked AI summary

Hybrid Semantic Context-Enhanced Ensemble Learning for Wind Power Ramp-Event Forecasting and Uncertainty-Aware Evaluation

Momina Liaqat Ali, Muhammad Abid, Muhammad Abdullah, Aneela Zameer

arXiv:2608.29024v1cs.LG

TL;DR

Wind-power ramp events are abrupt, variable output changes that are difficult to forecast and important for grid balancing and reserve planning. The paper adds LLM-derived semantic operational-context embeddings to conventional forecasting features, evaluating the approach across horizons, models, ramp definitions, datasets, and uncertainty measures. The semantic features provide small, statistically significant gains, particularly at 30 and 60 minutes, while GRU remains strongest for some longer-horizon ramp errors and prediction intervals weaken during ramps.

  • Problem

    Wind-power ramp events are abrupt, variable output changes that are difficult to forecast and complicate balancing, reserve planning, and system control.

  • Method

    The paper converts numerical and symbolic turbine operating states into short textual notes, embeds them semantically, and combines those embeddings with forecasting features in hybrid models.

  • Results

    Semantic enrichment produces small but statistically significant gains, especially at 30 and 60 minutes, while GRU remains strongest for longer-horizon ramp-event errors.

  • Takeaways & Limitations

    Operating context can enhance existing wind-power forecasts, but its value depends on the model and does not consistently surpass strong conventional or deep sequence models.

  • Takeaways & Limitations

    Prediction intervals cover the full test data well but underperform on ramp-event samples, and the main analysis is limited to SDWPF turbines numbered 20 or below.

Abstract

from arXiv · show

Wind power ramp events which are sudden, large swings in turbine output over short windows are difficult to estimate, and standard models often miss them. Hybrid forecasting approach is built which augments semantic context to ramp-event forecast. Rather than applying an extensive language model directly to predict turbine operating data, we have implemented a pipeline where turbine operating data is converted to simplified text, which is then converted to dense embeddings to be used as inputs for ensemble models incorporated with other features. Testing runs are performed at multiple intervals within the SDWPF dataset, including 10-minute, 30-minute, and 60- minute horizons, with ramp events constituting the highest change in future power output. We check robustness against autoregressive, LSTM, and GRU baselines plus several ensemble configurations, using Diebold-Mariano tests and bootstrap confidence intervals, and we vary the ramp threshold, compress the embeddings with PCA, and validate externally on Kaggle SCADA and NREL data with uncertainty-aware scoring. The semantic-context features produce negligible yet statistically significant gains over the baselines in multiple paired ensemble runs, most clearly at the 30- and 60-minute horizons where these gains hold across different ramp-threshold definitions, and PCA compression helps in some longer-horizon cases. The best context- augmented ensembles rank near the top overall, though the GRU model still posts the lowest ramp-event RMSE at 30 and 60 minutes. External tests confirm the error reduction generalizes across datasets, but the size of the gain depends on both model and dataset. Prediction intervals cover most test cases well but weaken during ramp events, pointing to a localized shift in the data distribution.

1 Introduction

The paper addresses difficult wind-power ramp forecasting by augmenting numerical forecasting with semantic operational context derived from turbine states. It evaluates this hybrid design across forecast horizons, model families, ramp definitions, statistical tests, dimensionality reduction, external datasets, and uncertainty estimation.

  • Motivation: Wind-power ramps are rapid output changes that complicate grid operation, reserve planning, and short-term forecasting.They are less frequent than normal periods and vary in magnitude, duration, and timing.
  • Proposed approach: The proposed method converts turbine and weather states into semantic operational context and uses LLM-derived embeddings as auxiliary features rather than as a direct forecaster.The workflow combines semantic representations with numerical, lagged, rolling, and other forecasting features.
  • Evaluation design: The experiments cover 10-, 30-, and 60-minute horizons, ramp-threshold sensitivity, PCA embedding compression, statistical robustness checks, and external Kaggle SCADA and NREL validation.The assessment framework separates overall forecasting performance from ramp-event performance and examines thresholds at the top 10%, 15%, and 20% of changes.
  • Findings: Semantic enrichment yields small but statistically supported gains, especially at 30 and 60 minutes, while GRU remains strongest for longer-horizon ramp-event errors.Rich-LLM ensembles remain competitive, but the semantic features generally enhance rather than replace conventional and deep sequence models.
  • Evaluation design: The study compares paired LLM and no-LLM configurations while holding the forecasting model family constant.It also benchmarks persistence, autoregressive, ensemble, boosting, LSTM, GRU, and uncertainty-aware models.

2 Related Work

Wind-power forecasting research spans physical, statistical, ensemble, recurrent, convolutional, attention-based, and hybrid approaches, while ramp-event studies increasingly evaluate event-specific behavior. This study adds semantic operational context by converting turbine states into text-derived representations and testing them alongside conventional predictors.

  • Wind-power forecasting methods include physical, statistical, ensemble, recurrent, convolutional, attention-based, and hybrid architectures.
  • Because ramp events are rare and variable in magnitude, duration, and timing, evaluation should distinguish ramp subsets from overall forecasting performance.
  • Ramp-event forecasting research covers regression, classification, probabilistic, attention, graph-based, and multi-task paradigms.
  • The study represents turbine operating context through numerical variables, symbolic states, text summaries, TF-IDF features, and dense embeddings.
  • Rather than forecasting directly with an LLM, the framework converts sensor readings into descriptive text, encodes that text into vectors, and supplies them to standard forecasting models.
  • The evaluation compares LLM-augmented and non-LLM models using ensemble settings, bootstrap confidence intervals, Diebold–Mariano tests, threshold sensitivity, PCA, and external validation.

3 Data and Problem Formulation

The study formulates multi-horizon turbine power forecasting and ramp-event evaluation using SDWPF data, external test sets, engineered temporal/context features, and leakage-aware temporal splits.

  • 3.1 Dataset Description: SDWPF provides turbine-level active power and environmental operating variables, while the main analysis uses turbines 1–20 sampled every 10 minutes.The primary forecasts target 10-, 30-, and 60-minute horizons; Kaggle SCADA and NREL data provide external test cases.
  • 3.3 Forecasting Problem Formulation: The forecasting target is future active power P_i,t+h for horizon h, with input vectors that may combine measurements, lags, rolling statistics, spatial-temporal variables, and context representations.The formulation allows numerical, symbolic, textual, rich semantic, and PCA-compressed semantic features depending on model specification.
  • 3.3 Forecasting Problem Formulation: Each turbine is split chronologically into 70% training, 15% validation, and 15% testing, while lagged and rolling features use only prior observations.This ordering prevents future information from entering forecasting features.
  • 3.4 Lagged, Rolling, and Context Features: Historical power is represented through lags at 1, 2, 3, 6, 12, and 24 time steps plus rolling means, standard deviations, and ramp-related context variables.The context includes wind-speed, power, and direction changes, variability, and operating-regime indicators.
  • 3.5 Semantic Operating-Context Representation: Operational text notes are converted into dense embeddings and joined with numerical forecasting inputs; full 384-dimensional and PCA-compressed versions are evaluated.PCA dimensions tested are d ∈ {16, 32, 64, 128}, targeting lower dimensionality and potentially reduced noise.
  • 3.6 Ramp-Event Definition: Ramp events are defined from the highest absolute future power changes, primarily the top 10% within each horizon, with percentile thresholds avoiding a fixed engineering cutoff.Sensitivity analysis also considers the top 15% and top 20% definitions.
  • 3.7 Evaluation Subsets: Evaluation separates all test samples from ramp-event samples to assess overall forecasting quality and performance during rapid operating changes.The distinction recognizes that acceptable aggregate performance can coexist with poor ramp-period performance.

4 Methodology

The methodology combines standard forecasting inputs with multiple operational-context views, using semantic embeddings as supplementary features rather than treating an LLM as the forecaster.

  • Forecasting Framework: The framework predicts P_i,t+h from original turbine, weather, temporal, spatial, lagged, rolling, and context features across h ∈ {1, 3, 6}.These horizons correspond to 10, 30, and 60 minutes.
  • Semantic Context Integration: Dense semantic embeddings are generated from short natural-language descriptions of turbine operating states and merged with conventional forecasting inputs.The descriptions include wind and power regimes, recent changes, direction shifts, variability, and ramp-risk context.
  • Numeric Features: Numeric features include turbine, weather, temporal, location, prior-power, and window-summary variables, with lagged and rolling quantities computed from past data only.These features represent recent direction, persistence, and short-run variability without future leakage.
  • Ramp Context Features: Ramp-oriented numeric context uses local changes in wind speed, active power, and wind direction, with circular direction transformations constrained to [−180°, 180°].Additional rolling wind-speed and power statistics capture rapid operating swings associated with ramp-prone periods.

4.3 Symbolic, Textual, and Semantic Context Representations

The context representation pipeline progresses from symbolic operating states to sparse text features and dense semantic embeddings, with PCA variants testing whether reduced representations retain useful information.

  • Symbolic Context: Numeric context variables are discretized into symbolic states covering wind, power, change, direction, and ramp-risk regimes.These states form the symbolic context vector used by later representations.
  • Textual Context: Concatenated symbolic states are transformed into sparse TF-IDF vectors to create a textual context representation.This provides a text-based alternative to the symbolic and dense semantic views.
  • Operational Summaries: Plain-language summaries describe numerical and symbolic turbine states, including levels, recent changes, variability, time of day, and ramp risk.A fixed text-generation function produces the operational summaries before embedding.
  • Semantic Embeddings: A pretrained sentence embedding model converts the operational summaries into rich semantic feature vectors for forecasting.The resulting dense representation is one of the context configurations compared in the study.

4.4 Paired No-LLM and Rich-LLM Forecasting Design

The paired design compares otherwise unchanged forecasting models with and without rich LLM-derived semantic features, isolating the contribution of semantic context.

  • Paired Model Design: The experiment runs each forecasting model in a no-LLM version that excludes z_LLM,i,t and a rich-LLM version that includes it.The paired comparison directly tests the incremental value of the semantic signal.
  • Controlled Comparison: The learning technique and model architecture remain unchanged between paired versions, so outcome differences are attributed to the semantic-embedding substitution.The same comparison strategy is applied to SDWPF, Kaggle SCADA, and NREL data.

4.5 Forecasting Models

The study evaluates traditional, machine-learning, boosting, sequence, and ensemble forecasting models with standard and ramp-weighted objectives. It compares paired model variants using squared-error losses, Diebold–Mariano tests, bootstrap intervals, and ramp-threshold sensitivity.

  • Model families: Forecasting models include moving-average variants, tree and boosting ensembles, and recurrent sequence models.The tree-based settings use raw, copula, and SVD-transformed feature sets.
  • Objectives: The study evaluates both standard squared-error loss and a ramp-weighted objective that increases the influence of high-change samples.The weighting strength is controlled by λ, while n denotes the absolute future power change.
  • Statistical comparison: Diebold–Mariano tests compare paired forecast methods using squared-error loss and a Newey-West adjustment for serial dependence.A positive mean loss difference indicates lower average squared error for the second model.
  • Uncertainty of gains: Bootstrap resampling estimates 95% intervals for paired RMSE improvements across models and forecast horizons.The interval uses the 2.5th and 97.5th percentiles of resampled RMSE gains.
  • Sensitivity analysis: Ramp sensitivity is assessed using the top 10%, 15%, and 20% of absolute future power changes.These thresholds test whether conclusions persist under stricter or more relaxed ramp definitions.

4.7 Uncertainty-Aware Quantile Forecasting

Uncertainty-aware forecasting uses conditional quantiles to construct prediction intervals and evaluates both interval coverage and width. Feature-group importance analysis separately measures how semantic and other feature classes contribute across forecast horizons.

  • Quantile forecasting: Quantile models learn the 0.10, 0.50, and 0.90 conditional quantiles of future active power.These quantiles support construction of an 80% prediction interval.
  • Interval evaluation: Prediction intervals are evaluated on all test cases and ramp-event cases using PICP and MPIW.The comparison checks whether uncertainty estimates remain stable as ramp rates increase.
  • Feature importance: Grouped feature importance partitions predictors into historical, physical, numerical-context, symbolic-context, text, semantic-embedding, PCA-embedding, time, and location classes.The grouped analysis tracks how semantic operating-context importance changes with forecast horizon.

4.9 Workflow Summary

The workflow converts turbine measurements into multiple feature representations, trains paired forecasting models, defines ramp events, and evaluates predictive, statistical, sensitivity, external-validation, and uncertainty outcomes.

  • Representation construction: Operating data are cleaned and transformed into lagged, rolling, temporal, spatial, numerical, symbolic, textual, and semantic representations.Natural-language summaries are encoded into rich embeddings, with PCA-compressed embeddings constructed separately.
  • Paired modeling: The workflow trains paired no-LLM and rich-LLM models after defining the future active-power target.It then evaluates full-test and ramp-event performance using multiple model classes.
  • Evaluation: Ramp samples use the top 10%, 15%, and 20% of absolute future power changes, followed by paired gains, Diebold–Mariano tests, bootstrap intervals, and quantile-interval metrics.The workflow repeats evaluation across the specified horizons and model settings.

5 Experimental Setup

The experimental setup compares forecasting models and context representations across three horizons, ramp definitions, datasets, and evaluation criteria. It combines chronological testing with paired semantic-context comparisons, significance tests, PCA sensitivity, external validation, and uncertainty metrics.

  • Forecast design: Forecasts target future active power at 10-, 30-, and 60-minute horizons using only information available at time t.The SDWPF sampling interval maps these horizons to forecast steps h ∈ {1, 3, 6}.
  • Evaluation subsets: Evaluation reports performance on the full test span and on ramp-event cases defined by the top 10% of absolute future power shifts.Sensitivity checks repeat the analysis with top 15% and top 20% definitions.
  • Data splitting: Chronological splits allocate 70% of each turbine sequence to training, 15% to validation, and 15% to testing without future-data leakage.Lagged and rolling inputs use only past values.
  • Context ablations: Context ablations compare XGBoost models using lagged, symbolic, TF-IDF, short-LLM, and rich-LLM context features.Semantic models append sentence-transformer vectors to numerical, lagged, rolling, and symbolic inputs.
  • Robustness testing: Paired semantic-context gains are assessed with RMSE comparisons, Diebold–Mariano tests, and bootstrap confidence intervals.The Newey-West correction addresses dependence in multi-step forecast errors, while bootstrap percentiles define 95% intervals.
  • Embedding compression: PCA sensitivity compares 384-dimensional rich embeddings with 16-, 32-, 64-, and 128-dimensional representations fitted on training data.The comparison tests whether compressed embeddings retain useful operational information.
  • External validation: Kaggle SCADA and NREL data provide external validation of no-LLM versus rich-LLM designs at all three forecast windows.Because NREL contains few high-horizon ramp samples, external datasets primarily assess overall weather-related durability rather than ramp-event results.
  • Metrics: Point forecasts use MAE, RMSE, sMAPE, and R2, while uncertainty evaluation uses PICP and MPIW on all-test and ramp-event samples.RMSE gains are expressed as percentage reductions after adding richer semantic embeddings.

6 Results and Discussion

Across ramp-event evaluations, semantic context generally improves forecasting, with gains that are small but often statistically supported and more consistent at longer horizons. External validation indicates error reductions transfer across datasets, while uncertainty coverage remains weaker for ramps.

  • Ramp-event evaluation: Top-10% ramp-event samples isolate periods where persistence deteriorates as active power changes rapidly.
  • Context ablation: Rich LLMContext-XGBoost reduces ramp-event RMSE relative to persistence by 4.43%, 8.12%, and 9.96% at 10-, 30-, and 60-minute horizons.
  • Context ablation: Rich LLM-derived context provides additional gains beyond numerical lags, symbolic context, and textual representations in the context ablation.
  • Paired ensemble comparison: Paired comparisons show improved ramp-event RMSE for three of five models at 10 minutes and all evaluated model families at 30 and 60 minutes.
  • Robustness analyses: Sensitivity analysis finds positive best LLM gains under top-10%, top-15%, and top-20% ramp definitions, generally increasing for broader top-20% samples.
  • Robustness analyses: PCA-64 reduces XGBoost ramp-event RMSE by 2.086% relative to no-LLM at 60 minutes, although full 384-dimensional embeddings often help at shorter horizons.
  • Model comparison: Rich-LLM ensembles are competitive but do not dominate: GRU records the lowest ramp-event RMSE at 30 and 60 minutes.
  • External validation: External RMSE gains range from 0.147% to 1.789% on Kaggle SCADA and from 2.967% to 11.805% on NREL across 10- to 60-minute horizons.

7 Discussion

The discussion presents semantic context as a useful but non-dominant enhancement whose value varies by model, horizon, and dataset. Point forecasts improve, but ramp-event uncertainty remains difficult to calibrate.

  • Discussion: Rich LLM-context effectiveness varies across model families, forecast horizons, and evaluation settings rather than consistently outperforming symbolic or text-based representations.
  • Discussion: Most context-enriched classification techniques improve significantly at 30 and 60 minutes, but the gains are generally small and complement rather than replace conventional regressors.
  • Discussion: Rich-LLM ensembles remain competitive, while deep sequence models retain distinct temporal information and SDWPF remains the primary basis for ramp-event evaluation.
  • Discussion: Prediction intervals cover the overall test data well but seriously underperform for ramp-event samples, indicating that abrupt changes require specialized uncertainty treatment.

8 Conclusion

The proposed hybrid scheme represents turbine operating modes as dense semantic embeddings and combines them with conventional features for ramp-event forecasting. Semantic context generally adds value, but gains depend on the model and validation setting, while strong baselines can remain superior.

  • The scheme converts turbine operating modes into natural-language descriptions, embeds them densely, and combines them with numerical, lagged, rolling, canonical, and text features.Forecasts are issued 10, 30, and 60 minutes ahead using the SDWPF database.
  • Semantic context generally adds some value, but its benefit depends on the model under consideration.The reported gains are not uniform across model configurations or forecast horizons.
  • Rich-LLM ensembles did not consistently outperform strong conventional and deep sequence models; GRU was the best single-task model for 1-hour ramps.A rich-LLM ensemble achieved more accurate 10-minute ramp-event results than GRU or other models, but GRU led for 1-hour ramps.
  • Combined Kaggle SCADA and NREL validation indicated RMSE reductions across evaluated horizons, although the reductions were concentrated mainly in ramps forecasted by the LLM-test database.The external validation also found uncertainty estimates appropriate on the samples, but uncertainty was not calibrated for ramp events.
Loading 2608.29024v1…