MATNet proposes a hybrid approach combining AI with physical knowledge for accurate PV generation forecasting. It uses historical PV data and weather forecasts through multi-level fusion. Results show significant improvement over existing methods, enhancing forecasting accuracy and supporting PV energy integration. The model's effectiveness is evaluated using the Ausgrid benchmark dataset with various regression metrics.
MATNet's architecture includes an embedding module, positional encoding, self-attention mechanism, dense interpolation layer, and multi-level joint fusion. Training details involve PyTorch framework on NVIDIA A100 GPU for 200 epochs. Evaluation metrics include RMSE, MAE, WMAPE, and MASE. MATNet surpasses LSTM, GRU, BiLSTM models in performance.
Ablation study reveals that incorporating multiple data sources boosts model performance significantly. Weather forecast input plays a crucial role in improving overall performance. Future work aims to develop an adaptive joint fusion layer and incorporate explainable AI paradigm for interpretability.
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