에너지 설비 안전관리를 위한 IoT와 빅데이터 활용방안 연구
A Study on IoT and Big Data Application for Energy Facility Safety Management
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
신재생에너지의 보급이 높아지면서 불규칙한 에너지 생산 문제를 해결하고 안전하게 설비를 관리하는 것이 중요하게 되었다. 태양광 설비의 경우 설비에서 화재가 발생하는 등 안전관리 문제가 대두 되었다. 문제 발생을 바로 탐지하거나 예측할 수 있도록 실시간으로 모니터링하고 이상 탐지 모델은 빅데이터 분석을 반영하여 계속 최신화된다. 안전관리가 필요한 가정, 공장에서는 IoT 디바이스를설치하면 실시간 탐지와 빅데이터 분석이 가능한 분산 적응형 아키텍처를 수립하였다. 컨테이너 기반의 애플리케이션 가상화 기술을 적용하여 사용량에 따라 즉시 증설이 가능하면서 가용성도 높인다. 이상 탐지를 위해 지도학습과 비지도학습 기반의 알고리즘이 활용 될 수 있으며, 설비 및 사용자로 인해 발생되는 문제를 발견하기 위해 중요한 요소이다. 본 연구에서는 태양광 설비의 안전관리를 위해전력 생산 및 환경정보를 IoT를 활용하여 수집하고 빅데이터를 활용하여 분석하기 위한 분산적응형아키텍처와 이상 탐지 모델의 구현 방안을 제시한다.
Abstract
As the spread of new and renewable energy increases, it has become important to solve the problem of irregular energy production and manage facilities safely. In the case of solar power facilities, safety management issues have emerged, such as a fire at the facility. By monitoring in real time, the occurrence of a problem can be detected or predicted immediately, and the anomaly detection model is continuously updated by reflecting big data analysis. In homes and factories that require safety management, we have established a distributed adaptive architecture that enables real-time detection and big data analysis by installing IoT devices. By applying containerbased application virtualization technology, it is possible to increase the capacity immediately depending on the usage and increase the availability. Supervised learning and unsupervised learning- based algorithms can be used to detect anomalies, and they are an important factor in discov-ering problems caused by facilities and users. This study proposes a distributed adaptive architecture and anomaly detection model implementation method to collect power generation and environmental information using IoT for safety management of solar power facilities and analyze using big data.
참고문헌 (37)
- Federal Energy Regulatory Commission, "Assessment of Demand Response & Advanced Metering", ~ (2008)
- E. Ancillotti, "The role of communication systems in smart grids: Architectures, technical solutions and research challenges", Computing Communication, 36 (2013)
- Il-Moo Lee, "A Study on the Safety Design Considerations for the fire Prevention of ESS", The Korean Institute of Electrical Engineers Conference Proceedings (2019)
- Jung-Ho Lee, "Localization Status and Expected Effects of EMS", Journal of the electric world (2015)
- Bass, Len, "Software Architecture in Practice" (2003), Addison Wesley
- P. Clements, "Documenting Software Architectures, Views and Beyond" (2002), Addison Wesley
- "LOLIN D1 mini Datasheet"[URL]
- Hyung-Jun Kim, "Cloud computing implementation technology" (2010), acornpub
- Amazon Web Services,Inc, "introduction of Amazon Web Services", ~ (2017)
- Miguel Fontanilla, "Helm: making kubernetes even better", cloud computing, ~ (2020)
- Marz, Nathan, "Big Data: Principles and best practices of scalable realtime data systems" (2013), Manning Publications
- Lara Irwin, "The 3-second rule you need to know, to engage customers with your mobile app", Sogeti UK BLOG, ~
- "Bigdata architecture", Micorsoft Azure guide, ~ (2018)
- G. Fenza, "Drift-Aware Methodology for Anomaly Detection in Smart Grid", IEEE Access, 7 (2019)
- X. Liu, "Regression-based online anomaly detection for smart grid data", arXiv preprint arXiv:1606.05781, ~ (2016)
- Y. Himeur, "A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks", Cognitive Computation, 12(6) (2020)
- B. R. Kiran, "An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos", Journal of Imaging, 4(2) (2018)
- R. Wang, "Deep learning for anomaly detection", WSDM ’20, Association for Computing Machinery (2020)
- Y. Weng, "Multi-agent-based unsupervised detection of energy consumption anomalies on smart campus", IEEE Access, 7 (2019)
- X. Wang, "Power consumption predicting and anomaly detection based on long short-term memory neural network", 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (2019)
- M. Munir, "Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models", Sensors, 19(11) (2019)
- S. Li, "Electricity theft detection in power grids with deep learning and random forests", Journal of Electrical and Computer Engineering, 2019(2) (2019)
- K. Alrawashdeh, "Toward an online anomaly intrusion detection system based on deep learning", 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (2016)
- M. N. Fekri, "Generating energy data for machine learning with recurrent generative adversarial networks", Energies, 13(1) (2020)
- E. Roberts, "Intelligent Systems Design and Applications" (2021), Springer International Publishing
- H. N. Akouemo, "Probabilistic anomaly detection in natural gas time series data", International Journal of Forecasting, 32(3) (2016)
- R. Kromanis, "Support vector regression for anomaly detection from measurement histories", Advanced Engineering Informatics, 27(4) (2013)
- P. Arjunan, "Multi-user energy consumption monitoring and anomaly detection with partial context information", Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, BuildSys ’15, Association for Computing Machinery (2015)
- J. Henriques, "Combining k-means and xgboost models for anomaly detection using log datasets", Electronics, 9(7) (2020)
- J. Yeckle, "Detection of electricity theft in customer consumption using outlier detection algorithms", 2018 1st International Conference on Data Intelligence and Security (ICDIS) (2018)
- P. Berezinski, "An entropybased network anomaly detection method", Entropy, 17(4) (2015)
- R. Chalapathy, "Anomaly detection using one-class neural networks", arXiv:1802.06360, ~ (2018)
- P. Oza, "One-class convolutional neural network", IEEE Signal Processing Letters, 26(2) (2019)
- L. Ruff, "Deep one-class classification", Vol. 80 of Proceedings of Machine Learning Research (2018), PMLR
- M. Valko, "Conditional anomaly detection with soft harmonic functions", 2011 IEEE 11th International Conference on Data Mining (2011)
- P. Naveen, "Cloud computing for energy management in smart grid - an application survey", IOP Conference Series: Materials Science and Engineering, 121 (2016)
- X. Xu, "Recent progress of anomaly detection", Complexity, ~ (2019)