A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm

•A hybrid load forecasting model with hyper-parameters optimization is proposed.•Nonlinear mapping is introduced to map the relevant factors.•Bayesian Optimization Algorithm (BOA) is used in hyperparameter optimization.•Proposed model periodically moves the data window which has high practicability....

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Vydáno v:Applied energy Ročník 237; s. 103 - 116
Hlavní autoři: He, Feifei, Zhou, Jianzhong, Feng, Zhong-kai, Liu, Guangbiao, Yang, Yuqi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.03.2019
Témata:
algorithms
Bayesian optimization algorithm
Bayesian theory
China
energy
Long short-term memory network
lymphocytes
prediction
processing technology
regression analysis
Relevant factors
Short-term load forecasting
temperature
Variational mode decomposition
China
Long short-term memory network
Variational mode decomposition
Relevant factors
Short-term load forecasting
Bayesian optimization algorithm
ISSN:0306-2619, 1872-9118
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Shrnutí:•A hybrid load forecasting model with hyper-parameters optimization is proposed.•Nonlinear mapping is introduced to map the relevant factors.•Bayesian Optimization Algorithm (BOA) is used in hyperparameter optimization.•Proposed model periodically moves the data window which has high practicability.•Proposed method is validated by seven contrast methods in Keras python framework. Short-term load forecasting plays an essential role in the safe and stable operation of power systems and has always been a vital research issue of energy management. In this research, a hybrid short-load forecasting method with Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks considering relevant factors which optimized by the Bayesian Optimization Algorithm (BOA) is studied. This method firstly decomposition with VMD which is a non-recursive signal processing technology that can decompose a signal into a discrete number of modes, then, consider the relevant factors and extend to the sequence according to the coefficient of association. Specifically, for the day type and higher or lower temperature, the nonlinear mapping is used and optimized by the BOA. Finally, the subsequences are predicted by LSTM which is a special Recurrent Neural Network with memory cells and reconstructed. To validate the performance of the proposed method, two categories of contrast methods including individual methods and decomposition-based methods are demonstrated in this study. The individual methods which without decomposition processes including LSTM, Support Vector Regression, Multi-Layered Perceptron Regressor, Linear Regression, and Random Forest Regressor, and the decomposition based methods including Empirical Mode Decomposition-Long Short-Term Memory, and Ensemble Empirical Mode Decomposition-Long Short-Term Memory. The simulation results, which developed in four periods of Hubei Province, China, show that the prediction accuracy of the proposed model is significantly improved compared with the contrast methods.
Bibliografie:ObjectType-Article-1
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2019.01.055