Fig 1.
Monthly wind power generation (Pawan Danawi Wind Farm – Sri Lanka) from 2015–2019.
The red dot marks the maximum output (3037.8 MWh) and the green dot the minimum (125.2 MWh), illustrating the high variability that grid operators must manage.
Fig 2.
Correlation heatmap illustrating the relationships between wind power generation and various climate indices.
The color gradient represents correlation values, with red indicating strong positive correlations and blue indicating strong negative correlations. The WIND and POWER variables exhibit a strong positive correlation (0.95), while other indices, such as AMO, ENSO, and MEI, show varying degrees of influence on wind power. This analysis helps identify key climate factors that affect wind power variability.
Fig 3.
Lagged correlation analysis between wind power generation and various climate indices.
Each subplot shows the correlation coefficients for different lag periods (in months), with the highest correlation highlighted in red. The best lag for each climate index is indicated, demonstrating the temporal influence of climate variability on wind power. Notably, WIND vs. POWER exhibits the strongest correlation (0.88) at a 12-month lag, while other indices such as AMM, AMO, NAO, and PDO show moderate correlations at varying lag periods. This analysis helps identify the optimal time delay for incorporating climate indices into wind power forecasting models.
Fig 4.
Comparison of predictive performance for 25 candidate models across four test conditions.
Each pane displays models sorted by increasing RMSE (x-axis) with the model number on the y-axis, and bars are colored by RMSE magnitude (colorbar). Panels are arranged as (a) Experiment A, (b) Experiment B, (c) Experiment C, and (d) Experiment D. Lower RMSE (dark purple) indicates better accuracy, while higher RMSE (yellow) highlights poorer performance.
Fig 5.
Coefficient of determination (R²) for the top-performing models on the validation (blue circles) and test (orange squares) sets across four experiments (A–D).
Overlaid red and green rings indicate the model IDs selected for each validation and test point, respectively. Higher R2 values denote stronger predictive accuracy.
Fig 6.
SHAP summary dot-plot for the three top MRMR-selected predictors (WIND, AMM_9, WIND_6).
Each point represents one forecast instance, plotted by its Shapley value (x–axis) to show the feature’s impact on the model’s output, and colored by the corresponding predictor value (blue = low, yellow = high). The current wind speed (WIND) dominates model behavior; higher WIND values consistently push predictions upward, while the nine-month Atlantic Meridional Mode index (AMM_9) and the six-month lagged wind (WIND_6) exert more moderate positive and negative influences, respectively.
Table 1.
Best-performing models on the validation set.
Table 2.
Best-performing models on the test set.
Table 3.
Model specifications across the four experimental cases.