AINeutralarXiv – CS AI · 10h ago6/10
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Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation
Researchers present a Transformer Autoencoder framework with local attention mechanisms designed to detect non-technical losses (electricity theft) in power grids using sparse, irregular time series data. The model demonstrates superior performance in risk estimation for Greek electrical systems compared to existing methods, achieving high recall and precision while effectively handling data collection irregularities.