融合天气因素的多尺度负荷特征提取，提升光伏发电分离精度
Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion
- 用分层插值提取净负荷时序特征
- 通过多头自注意力捕捉天气因素复杂关联
- 适合需要精准分布式能源监控的电网公司
随着能源互联网与系统集成的发展，分布式光伏（PV）系统普及带来智能监测新挑战，尤其在分离光伏发电与净用电负荷方面。现有方法在净负荷特征提取和天气因素相关性捕捉上存在不足。本文提出一种结合分层插值（HI）与多头自注意力机制的光伏拆分方法。通过HI提取净负荷时序特征，利用多头自注意力建模天气因素间的复杂依赖关系，实现高精度光伏发电预测。仿真实验验证了该方法在真实数据中的有效性，有助于提升分布式能源系统的监测与管理能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。