Error Distribution-based Anomaly Score for Forecasting-based Anomaly Detection of PV Systems
For forecasting-based anomaly detection for PV systems, in this paper, we propose a way for calculating anomaly scores. The basic idea of our approach is to utilize the distribution of forecasting errors of normal data to derive relative anomaly score, which is limited to from 0 to 100. To further i...
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Published in | 2021 International Conference on Information and Communication Technology Convergence (ICTC) pp. 1144 - 1146 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
20.10.2021
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Subjects | |
Online Access | Get full text |
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Summary: | For forecasting-based anomaly detection for PV systems, in this paper, we propose a way for calculating anomaly scores. The basic idea of our approach is to utilize the distribution of forecasting errors of normal data to derive relative anomaly score, which is limited to from 0 to 100. To further improve the anomaly score, we apply our basic idea to each month separately because the distribution of forecasting errors changes over time. Through experiments using the real data, we examine some aspects of our approach preliminarily. |
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DOI: | 10.1109/ICTC52510.2021.9620808 |