Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion
This addresses monitoring challenges for utility companies due to distributed PV systems, but it appears incremental as it builds on existing feature extraction and attention techniques.
The paper tackled the problem of separating photovoltaic (PV) generation from net electricity load for utility companies by proposing a method that integrates Hierarchical Interpolation and multi-head self-attention mechanisms, achieving precise PV generation predictions as demonstrated in simulation experiments on real-world data.
With the advancement of energy Internet and energy system integration, the increasing adoption of distributed photovoltaic (PV) systems presents new challenges on smart monitoring and measurement for utility companies, particularly in separating PV generation from net electricity load. Existing methods struggle with feature extraction from net load and capturing the relevance between weather factors. This paper proposes a PV disaggregation method that integrates Hierarchical Interpolation (HI) and multi-head self-attention mechanisms. By using HI to extract net load features and multi-head self-attention to capture the complex dependencies between weather factors, the method achieves precise PV generation predictions. Simulation experiments demonstrate the effectiveness of the proposed method in real-world data, supporting improved monitoring and management of distributed energy systems.