Table 3.
Architectures and input–output dimensions of the proposed models.
| Model | Component / Layer | Type | Input Dimension | Output Dimension | Activation / Description |
|---|---|---|---|---|---|
| GBR | Input | Regression Tree Ensemble | 10 features | 1 | Gradient boosting with CART base learners |
| XGB | Input | Boosted Trees | 10 features | 1 | Regularized gradient boosting (tree-based) |
| LGBM | Input | Histogram-based Boosted Trees | 10 features | 1 | Leaf-wise tree growth |
| CatBoost | Input | Oblivious Tree Ensemble | 10 features | 1 | Ordered boosting, categorical handling |
| MLP | Input | Dense | 10 | 10 | – |
| LSTM | Hidden-1 | Dense | 10 | 64 | ReLU |
| Hidden-2 | Dense | 64 | 32 | ReLU | |
| Output | Dense | 32 | 1 | Linear | |
| Input | LSTM | (1 × 10) | 64 | Tanh | |
| Output | Dense | 64 | 1 | Linear | |
| Stacked Ensemble | Base Learners | GBR, XGB, LGBM, CatBoost, MLP | N × 10 (per model) | N × 1 | Individual model predictions |
| Meta-learner | Ridge Regression | N × M | N × 1 | Linear fusion of base predictions | |
| Final Output | Regression | – | 1 | Final PV power forecast |