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%.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-122568 |
| Date | January 2023 |
| Creators | Svensson, Robin, Shalabi, Saleh |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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