Photovoltaic Transformer Module Model

Transformer-based models, often combined with LSTM or other hybrid architectures, are currently leading approaches for accurate photovoltaic (PV) module output prediction and power forecasting.

Overview of Transformer-Based PV Models

Transformer-based models leverage attention mechanisms to capture complex dependencies in PV system data. These models are particularly effective in handling temporal and multivariate interactions, such as the relationship between PV power output and weather variables, irradiance, and temperature.

Key Architectures

1. Hybrid Transformer–BiLSTM Model
   • Combines the global attention of Transformers with the local dependency capture of BiLSTM.
   • Uses multi-type PV module datasets, often preprocessed with clustering methods like K-means++ to improve generalization.
   • Achieves high predictive accuracy, with coefficients of determination (R²) exceeding 0.989, outperforming standalone Transformer, BiLSTM, SVM, and Transformer–SVM models .
2. iTransformer–LSTM with Cross-Attention and KAN Mapping
   • The iTransformer extracts features from target variables, while LSTM processes covariates.
   • A cross-attention mechanism fuses outputs, followed by a Kolmogorov–Arnold Network (KAN) for enhanced representation.
   • Validated on seasonal PV datasets, this model captures seasonal variations and improves forecasting accuracy .
3. PV-Client (Enhanced Transformer)
   • Employs an Enhanced Transformer module to capture cross-variable dependencies between PV power and weather factors.
   • Integrates a linear module to learn trend information and simplifies embedding and position encoding layers.
   • Demonstrates state-of-the-art performance across multiple real-world PV datasets, surpassing GRU and SVR models in MSE and accuracy metrics .

Advantages of Transformer-Based PV Models

• High Accuracy: Hybrid and enhanced Transformer models consistently outperform traditional machine learning models in PV output prediction.
• Multivariate Feature Capture: Cross-variable attention allows the model to consider interactions between PV output and environmental factors.
• Seasonal Adaptability: Models like iTransformer–LSTM effectively handle seasonal variations in PV generation.
• Scalability: These models can be applied to large PV datasets from multiple locations, supporting grid integration and energy management.

Applications

• PV Power Forecasting: Accurate short-term and long-term predictions for grid management.
• Maximum Power Point Tracking (MPPT): Optimizing PV system efficiency.
• Energy Management Systems: Integrating PV output predictions into smart grids and renewable energy planning. Transformer-based PV module models represent a cutting-edge approach in renewable energy research, combining deep learning techniques with domain-specific knowledge to enhance PV system performance and forecasting reliability.