Unsupervised disaggregation of aggregated net load considering behind-the-meter PV based on virtual PV sample construction

Ziyu Qu, Xinxin Ge, Jinling Lu and Fei Wang

Applied Energy, 2025, vol. 381, issue C, No S0306261924023912

Abstract: Most of the distributed photovoltaics (PV) are installed behind the meter (BTM), single-meter deployments permit distribution system operators to monitor only the net load and exclude the BTM PV generation, so the growing prevalence of BTM PV installations negatively affects distribution system planning and the local balance of supply and demand. However, existing methods for net load disaggregation mainly rely on the installation of expensive monitoring devices and high-resolution sensors, and face challenges such as privacy concerns, data diversity, and communication barriers. In this paper, an unsupervised method for aggregated net load disaggregation is proposed that achieves accurate separation of BTM PV outputs and actual loads using only net load data and exogenous variables. First, a data-driven method is developed to construct the actual load sample matrix of customers. Then, a virtual PV sample construction method based on the self-feedback decoupling algorithm (SFDA) is proposed to tackle the invisibility of BTM PV resources. The method performs self-feedback learning and constructs the virtual PV samples by minimizing the long-term decomposition residuals, and generates the virtual PV sample matrix. Finally, the model learning results are employed to achieve net load disaggregation through the contextually supervised source separation (CSSS) algorithm. The study utilized real open-source data whereby analyses reveal the method greatly enhances the decoupling accuracy of unsupervised algorithms. Furthermore, it eliminates a series of problems associated with traditional supervised algorithms and expands the scope of unsupervised decoupling methods.

Keywords: Net load disaggregation; Behind-the-meter PV estimates; Unsupervised algorithms; Virtual PV sample construction; Self-feedback decoupling algorithm; Contextually supervised source separation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.125007

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