A hybrid data-driven online solar energy disaggregation system from the grid supply point

Abstract

Abstract 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.

Keywords

• Behind-the-metre solar generation
• Rooftop PV
• Deep neural network
• Transfer learning
• Energy disaggregation
• Data-driven analysis