Streaming Data Anomaly Detection in Energy Big Data Center Using Unsupervised Learning

The energy big data center plays a pivotal role in supporting real-time monitoring and decision-making. The complexity and dynamic nature of data streams makes anomaly detection a critical task in such systems. This study proposes an unsupervised learning framework, Unsupervised Learning-based Streaming Anomaly Detection (UL-SAD), for time-series anomaly detection in streaming data from the energy big data center. The framework integrates modules for data preprocessing, fluctuation analysis, dual-phase smoothing, and automated thresholding based on the Generalized Pareto Distribution (GPD). It achieves efficient anomaly detection in high-dimensional, noisy environments without relying on labeled datasets. Experimental results demonstrate that UL-SAD achieves an anomaly detection accuracy of 88% on multidimensional energy datasets, significantly improving operational efficiency and accuracy.

• Publication year

  2025

• Venue

  Chinese Control and Decision Conference

• Publication date

  2025-05-16

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/CCDC65474.2025.11090792
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning

  2024cited by this paper
• A review on multi-view learning

  2024cited by this paper
• Multidimensional information fusion and broad learning system-based condition recognition for energy pipeline safety

  2024cited by this paper
• Adaptive Memory Broad Learning System for Unsupervised Time Series Anomaly Detection

  2024cited by this paper
• Exploring Feature Selection With Limited Labels: A Comprehensive Survey of Semi-Supervised and Unsupervised Approaches

  2024cited by this paper
• Transformer-based multivariate time series anomaly detection using inter-variable attention mechanism

  2024cited by this paper
• Localizing and tracking of in-pipe inspection robots based on distributed optical fiber sensing

  2024cited by this paper
• Distributed Optical Fiber Intrusion Detection by Image Encoding and SwinT in Multi-Interference Environment of Long-Distance Pipeline

  2023cited by this paper
• Robust Tensor Low-Rank Sparse Representation With Saliency Prior for Hyperspectral Anomaly Detection

  2023cited by this paper
• When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection

  2023cited by this paper
• Deep Learning for Time Series Anomaly Detection: A Survey

  2022cited by this paper
• RETRACTED: Sequential tree recognition method of sensitive data in energy big data center based on rule matching

  2022cited by this paper
• Anomaly Detection for Time Series Data Stream

  2021cited by this paper
• On the Improvement of the Isolation Forest Algorithm for Outlier Detection with Streaming Data

  2021cited by this paper
• Anomaly Detection for IoT Time-Series Data: A Survey

  2020cited by this paper
• A Review on Outlier/Anomaly Detection in Time Series Data

  2020cited by this paper
• DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

  2019cited by this paper
• Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

  2018cited by this paper
• Anomaly Detection in Streams with Extreme Value Theory

  2017cited by this paper
• Streaming data anomaly detection method based on hyper-grid structure and online ensemble learning

  2016cited by this paper

• No citing papers are available for this paper.