The integration of small-scale Photovoltaics (PV) systems (such as rooftop PVs) decreases the visibility of power systems, since the real demand load is masked. Most rooftop systems are behind the metre and cannot be measured by household smart meters. To overcome the challenges mentioned above, this paper proposes an online solar energy disaggregation system to decouple the solar energy generated by rooftop PV systems and the ground truth demand load from net measurements. A 1D Convolutional Neural Network (CNN) Bidirectional Long Short-Term Memory (BiLSTM) deep learning method is used as the core algorithm of the proposed system. The system takes a wide range of online information (Advanced Metering Infrastructure (AMI) data, meteorological data, satellite-driven irradiance, and temporal information) as inputs to evaluate PV generation, and the system also enables online and offline modes. The effectiveness of the proposed algorithm is evaluated by comparing it to baselines. The results show that the proposed method achieves good performance under different penetration rates and different feeder levels. Finally, a transfer learning process is introduced to verify that the proposed system has good robustness and can be applied to other feeders.

小型光伏 (PV) 系统（例如屋顶光伏）的集成降低了电力系统的可见性，因为实际需求负载被掩盖了。大多数屋顶系统都位于电表后面，无法通过家用智能电表进行测量。为了克服上述挑战，本文提出了一种在线太阳能分解系统，以将屋顶光伏系统产生的太阳能与净测量的地面真实需求负载分离。一维卷积神经网络（CNN）双向长短期记忆（BiLSTM）深度学习方法被用作所提出系统的核心算法。该系统将广泛的在线信息（高级计量基础设施 (AMI) 数据、气象数据、卫星驱动的辐照度和时间信息）作为输入来评估光伏发电，并且系统还支持在线和离线模式。通过将其与基线进行比较来评估所提出算法的有效性。结果表明，所提出的方法在不同的穿透率和不同的馈线水平下取得了良好的性能。最后，引入迁移学习过程以验证所提出的系统具有良好的鲁棒性，并且可以应用于其他馈线。