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A Hybrid Data-Driven Online Solar Energy Disaggregation System from the Grid Supply Point
  • Xiao-Yu Zhang ,
  • Chris Watkins ,
  • Stefanie Kuenzel
Xiao-Yu Zhang
Royal Holloway

Corresponding Author:[email protected]

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Chris Watkins
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Stefanie Kuenzel
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The integration of small-scale PV systems (such as roof-top PVs) decreases the visibility of the power system since the real demand load is masked. Most of the rooftop systems are behind-the-meter and cannot be measured by the household smart meter. To overcome the challenges mentioned above, this paper proposes an online solar energy disaggregation system to decouple the solar energy generated by the roof-top PV systems and ground truth demand load from the net measurements. A 1D CNN bidirectional long short-term memory (CNN-BiLSTM) deep learning method is used as the core algorithm of the proposed system. The system takes a wide range of online information (AMI data, meteorological data, satellite-driven irradiance, and temporal information) as inputs to evaluate the 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 reaches good performance under different penetration rates and different feeder levels. Finally, a transfer learning process is introduced to verify the proposed system has good robustness and can be applied to anywhere else easily.
Aug 2023Published in Complex & Intelligent Systems volume 9 issue 4 on pages 3695-3716. 10.1007/s40747-022-00842-2