Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders
The implementation of smart grids introduces complexities where data quality issues, particularly outliers, pose significant challenges to accurate data analysis. This work develops an integrated methodology for the detection and correction of outliers in energy demand data, based on Artificial Neur...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/24/6403 |
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