Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data
Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes� energy consumption data. From the literature, it has been identified that the data imputation...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
2022
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| Online Access: | http://scholars.utp.edu.my/id/eprint/34027/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143847225&doi=10.3390%2fs22239323&partnerID=40&md5=d5ddaea488606fc2c5cf16c497ffac7d |
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