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Behind-the-Meter Load and PV Disaggregation via Deep Spatiotemporal Graph Generative Sparse Coding With Capsule

Mohsen Saffari, Mahdi Khodayar, Mohammad E Khodayar

    IEEE Transactions on Neural Networks and Learning Systems
    |June 20, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel spatiotemporal graph sparse coding capsule network for precise behind-the-meter (BtM) load and photovoltaic (PV) generation estimation. The method significantly improves accuracy in residential energy disaggregation.

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    Area of Science:

    • Electrical Engineering
    • Artificial Intelligence
    • Sustainable Energy

    Background:

    • Rooftop photovoltaic (PV) panels are crucial for sustainable energy, but their integration impacts distribution grids.
    • Accurate estimation of behind-the-meter (BtM) load and PV power is vital for grid operation due to customer load profile changes.

    Purpose of the Study:

    • To develop an advanced method for accurate BtM load and PV generation estimation in residential areas.
    • To address the challenges posed by the integration of distributed PV systems in distribution networks.

    Main Methods:

    • Proposes a spatiotemporal graph sparse coding (SC) capsule network integrating SC with deep generative graph modeling and capsule networks.
    • Models neighboring residential units as a dynamic graph, using spectral graph convolution (SGC) attention peephole long short-term memory (PLSTM) for pattern extraction.
    • Learns a dictionary for sparse representation in the encoder-decoder's hidden layer to enhance latent space sparsity.

    Main Results:

    • Achieved over 9.8% and 6.3% root mean square error (RMSE) improvements in BtM PV and load estimation, respectively, on the Pecan Street and Ausgrid datasets.
    • Demonstrated superior performance compared to existing state-of-the-art methods in energy disaggregation tasks.

    Conclusions:

    • The proposed SC capsule network effectively estimates BtM PV generation and residential load.
    • This approach offers a significant advancement for the operational management of distribution systems with high PV penetration.