Daily natural gas load forecasting based on sequence autocorrelation

Xiang, Xingren; Shen, Jian; Yang, Kaixiang; Zhang, Guoming; Qian, Jiren; Zhu, Chengyuan

doi:10.1109/yac57282.2022.10023872

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…In the context of natural gas load forecasting [37], where autocorrelation functions play a pivotal role, the superior performance of the designed prediction model opens avenues for combining these insights with cutting-edge DL optimization strategies. The effectiveness of autocorrelation in improving forecasting accuracy aligns with the objectives of DL optimizers such as cyclic learning rate, which aim to discover optimal learning rates for the training of neural networks.…”

Section: Autocorrelationmentioning

confidence: 99%

AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

Arthur,

Yudistira,

Dewi

2024

IEEE Access

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Time series prediction, a pivotal component in various domains, faces the intricate task of capturing complex patterns and dependencies within sequential data. Autocyclic Learning Rate (Auto-Cyclic) emerges as a groundbreaking solution, seamlessly integrating cosine cyclic learning rates with a meticulous emphasis on autocorrelation and variance. In the realm of time series forecasting, where longterm dependencies and dynamic patterns pose significant challenges, AutoCyclic represents a transformative approach. By dynamically adjusting learning rates based on the nuanced characteristics of time series data, AutoCyclic not only addresses issues such as underfitting and overfitting, but also provides exceptional robustness to outliers. Through rigorous evaluations on diverse datasets, including ETTm2, M4, and WindTurbine, AutoCyclic consistently outperforms traditional optimizers such as Adams Optimizer and Cosine Cyclic Learning Rate, showcasing the lowest Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) values. What sets AutoCyclic apart is not only its predictive accuracy, but also its astute exploitation of autocorrelation, which provides a unique advantage in discerning and leveraging inherent patterns in time series data. This intrinsic capability positions AutoCyclic as a transformative tool, redefining the landscape of time series forecasting methodologies and opening new dimensions in the utilization of autocorrelation for more insightful and accurate predictions.

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Section: Autocorrelationmentioning

confidence: 99%

AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

Arthur,

Yudistira,

Dewi

2024

IEEE Access

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“…Among them, energy transportation is an im-portant part of energy safety. Pipelines undertake the majority of energy transportation capacity, including oil, natural gas, hydrogen, etc (Xiang et al 2022). Especially in the oil and gas field, according to statistics, 70% of crude oil in the United States is transported through pipelines, and the total length of global energy pipelines has exceeded 4 million kilometers.…”

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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