☆ 4.6 Article

Stride-TCN for Energy Consumption Forecasting and Its Optimization

APPLIED SCIENCES-BASEL (2022)

Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks

Dan Li et al.

Summary: This paper presents a hybrid decomposition method combining EPT and CEEMDAN for wind speed prediction, integrated with TCN for individual component forecasting. Experimental results demonstrate the significant advantages of this approach in accuracy and stability.

ENERGY (2022)

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Article Engineering, Electrical & Electronic

Temporal convolutional networks interval prediction model for wind speed forecasting

Zhenhao Gan et al.

Summary: The wind speed interval prediction model based on temporal convolutional networks shows significant improvements in both prediction accuracy and reliability, with an adaptive interval construction optimization strategy proposed for training labels. The results suggest that the model can be a practical tool for wind speed forecasting.

ELECTRIC POWER SYSTEMS RESEARCH (2021)

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Article Engineering, Industrial

A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings

Yudong Cao et al.

Summary: The study introduces a new deep learning framework TCN-RSA for predicting the remaining useful life of mechanical systems, and demonstrates its superiority in experiments.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)

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Article Physics, Fluids & Plasmas

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

R. M. Churchill et al.

PHYSICS OF PLASMAS (2020)

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Stride-TCN for Energy Consumption Forecasting and Its Optimization

相关参考文献

Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks

Temporal convolutional networks interval prediction model for wind speed forecasting

A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

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