A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

被引:15
作者
Jung, Se-Hoon [1 ]
Huh, Jun-Ho [2 ]
机构
[1] Youngsan Univ Yangsan, Sch Connect Major Bigdata Convergence, Yangsan 50501, South Korea
[2] Catholic Univ Pusan, Dept Software, Busan 46252, South Korea
基金
新加坡国家研究基金会;
关键词
altered K-means; A-Deep Q Learning; big data analysis; transmission line tower big data; artificial intelligence; reinforcement learning; machine learning; !text type='Python']Python[!/text; DATA ANALYTICS; OUTLIER DETECTION; ENERGY MANAGEMENT; DEMAND RESPONSE; SMART; SYSTEMS; CLASSIFICATION; IMPROVEMENT; GENERATION; ALGORITHM;
D O I
10.3390/su11133499
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%similar to 4.19% higher prediction rate and around 0.8% similar to 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.
引用
收藏
页数:25
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