Open AccessPermutation test and bootstrap methods for unsupervised detection and estimation of behind‐the‐meter photovoltaic generation
Author(s) -
Liu Chao,
Shi Jing,
Chen Hongkun,
Chen Lei,
Li Guocheng
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12067
Subject(s) - permutation (music) , photovoltaic system , resampling , metre , computer science , estimation , test statistic , statistics , statistical hypothesis testing , artificial intelligence , mathematics , electrical engineering , engineering , physics , systems engineering , acoustics , astronomy
The penetration of unmonitored behind‐the‐meter photovoltaic systems has increased rapidly over the past decade. While unsupervised methods have been proposed to estimate behind‐the‐meter photovoltaic generation, their performance is significantly affected by hyperparameters. Existing methods for choosing hyperparameters require labelled data, which are unavailable to system operators. In this paper, a data‐driven method is proposed to generate labelled data to optimize the hyperparameters for unsupervised estimation of behind‐the‐meter photovoltaic generation. First, a permutation‐test based method is developed to detect photovoltaic installation in an unsupervised way. Second, consumers without PV are combined with limited monitored photovoltaic sites to simulate the original system through the bootstrap method. The simulated system provides labelled data for hyperparameter optimization. Finally, the near‐optimal hyperparameters are searched on the simulated system and applied to the original system's estimation. Through experiments on a smart meter dataset, the effectiveness of the proposed methodology is verified.