Anomalous energy consumption detection using a Naïve Bayes approach
نویسندگان
چکیده
Background: Industrial energy management has emerged as an important component in monitoring consumption particularly with the recent trend of migrating towards IR 4.0. The capability to detect anomalies is essential it serves a precautionary step for real-time response mitigate maximum demand penalty. purpose this research was develop high accuracy detection algorithm identify data recorded by smart meter. Methods: proposed utilized supervised and unsupervised machine learning techniques, namely Isolation Forest Gaussian Naïve Bayes. were first labeled using categorize them into normal abnormal groups. This followed Bayes classify predict meter reading. Results: These techniques showed significant predicting readings. used simulated collected less than month 30-minute reading intervals. divided testing validation sets according ratio 7:3. balanced score each different above 89%. average precision, recall F1 98%, 99% respectively. Whereas corresponding scores set 95%, 90% 92%. Conclusions: hybrid approach based on provided satisfactory anomaly electricity study presents quick simple method categorizing or abnormal, which assists automatically labelling vast datasets establishes fundamental framework occurrence industrial system.
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ژورنال
عنوان ژورنال: F1000Research
سال: 2022
ISSN: ['2046-1402']
DOI: https://doi.org/10.12688/f1000research.70658.1