An Ensemble Broad Learning Scheme for Short-Term Load Forecasting

Yang, Kaixiang; Wang, Haozhe; Yang, Qinmin; Shi, Yifan; Zhu, Chengyuan; Bi, Jichao

doi:10.1109/cac57257.2022.10056068

Cited by 1 publication

(1 citation statement)

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“…On the other hand, the device is easy to deploy in stations along the pipeline. The storage and invocation of data, as well as the training and updating of algorithms, have become convenient (Zhu et al 2022;Yang et al 2022b).…”

Section: Introductionmentioning

confidence: 99%

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

Zhu,

Yang,

Yang

et al. 2024

AAAI

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The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.

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Section: Introductionmentioning

confidence: 99%