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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Advanced Electricity Meter Anomaly Detection : A Machine Learning Approach

Svensson, Robin, Shalabi, Saleh January 2023 (has links)
The increasing volume of smart electricity meter readings presents a challenge forelectricity providing companies in accurately validating and correcting the associated data. This thesis attempts to find a possible solution through the application ofunsupervised machine learning for detection of anomalous readings. Through thisapplication there is a possibility of reducing the amount of manual labor that is required each month to find which meters are necessary to investigate. A solution tothis problem could prove beneficial for both the companies and their customers. Itcould increase abnormalities detected and resolve any issues before having a significant impact. Two possible algorithms to detect anomalies within these meters areinvestigated. These algorithms are the Isolation Forest and a Autoencoder, wherethe autoencoder showed results within the expectations. The results shows a greatreduction of the manual labor that is required up to 96%.

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