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Load forecasting method based on CEEMDAN and TCN-LSTM.

Luo Heng^1,2, Cheng Hao¹, Liu Chen Nan¹

¹School of Electronics and Information Engineering, University of Science and Technology of Suzhou, Suzhou, Jiangsu, China.

|July 5, 2024

Summary

This summary is machine-generated.

This study introduces an improved power load forecasting method using Completely Integrated Empirical Modal Decomposition (CEEMDAN) and Temporal Convolutional Networks-Long Short-Term Memory (TCN-LSTM) networks. The novel approach enhances forecasting accuracy and stability for volatile power loads.

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Area of Science:

Electrical Engineering
Data Science
Applied Mathematics

Background:

Power load forecasting faces challenges due to high stochasticity and volatility.
Accurate forecasting is crucial for efficient power grid management and stability.

Purpose of the Study:

To propose a novel power load forecasting method addressing accuracy and stability issues.
To leverage advanced signal decomposition and deep learning for improved predictions.

Main Methods:

Utilizing Completely Integrated Empirical Modal Decomposition (CEEMDAN) to decompose raw load data into stable subsequences.
Employing sample entropy for subsequence reorganization.
Applying a Temporal Convolutional Networks-Long Short-Term Memory (TCN-LSTM) model for feature extraction and prediction.

Main Results:

The CEEMDAN-TCN-LSTM method demonstrated higher accuracy compared to traditional forecasting techniques.
The proposed model showed a superior prediction effect in load forecasting tasks.
Validation was performed using electricity compliance data from New South Wales, Australia.

Conclusions:

The CEEMDAN-TCN-LSTM method offers a significant improvement in power load forecasting accuracy and stability.
This approach provides a valuable reference for future electricity load forecasting research and application.