Load forecasting method based on CEEMDAN and TCN-LSTM

Aiming at the problems of high stochasticity and volatility of power loads as well as the difficulty of accurate load forecasting, this paper proposes a power load forecasting method based on CEEMDAN (Completely Integrated Empirical Modal Decomposition) and TCN-LSTM (Temporal Convolutional Networks...

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Bibliographic Details
Published in:PloS one 2024-07, Vol.19 (7), p.e0300496
Main Authors: Heng, Luo, Hao, Cheng, Nan, Liu Chen
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Biology and Life Sciences
Computer and Information Sciences
Decomposition
Deep learning
Electrical loads
Electricity
Engineering and Technology
Entropy
Forecasting
Forecasting - methods
Machine learning
Methods
Modelling
Neural networks
Neural Networks, Computer
New South Wales
Physical Sciences
Predictions
Research and Analysis Methods
Stochasticity
Time series
Wavelet transforms
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Summary:Aiming at the problems of high stochasticity and volatility of power loads as well as the difficulty of accurate load forecasting, this paper proposes a power load forecasting method based on CEEMDAN (Completely Integrated Empirical Modal Decomposition) and TCN-LSTM (Temporal Convolutional Networks and Long-Short-Term Memory Networks). The method combines the decomposition of raw load data by CEEMDAN and the spatio-temporal modeling capability of TCN-LSTM model, aiming to improve the accuracy and stability of forecasting. First, the raw load data are decomposed into multiple linearly stable subsequences by CEEMDAN, and then the sample entropy is introduced to reorganize each subsequence. Then the reorganized sequences are used as inputs to the TCN-LSTM model to extract sequence features and perform training and prediction. The modeling prediction is carried out by selecting the electricity compliance data of New South Wales, Australia, and compared with the traditional prediction methods. The experimental results show that the algorithm proposed in this paper has higher accuracy and better prediction effect on load forecasting, which can provide a partial reference for electricity load forecasting methods.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0300496