Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management
Introduction: This paper proposes a deep learning algorithm based on the VMD-SSA-BiLSTM model for time series forecasting in the smart grid financial market. The algorithm aims to extract useful information from power grid signals to improve the timing prediction accuracy and meet the needs of susta...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-07-01
|
| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1239542/full |
| _version_ | 1797772790165667840 |
|---|---|
| author | Chengran Yin Guangming Wang Jiacheng Liao |
| author_facet | Chengran Yin Guangming Wang Jiacheng Liao |
| author_sort | Chengran Yin |
| collection | DOAJ |
| description | Introduction: This paper proposes a deep learning algorithm based on the VMD-SSA-BiLSTM model for time series forecasting in the smart grid financial market. The algorithm aims to extract useful information from power grid signals to improve the timing prediction accuracy and meet the needs of sustainable innovation management.Methods: The proposed algorithm employs the variational mode decomposition (VMD) method to decompose and reduce the dimensionality of historical data, followed by singular spectrum analysis (SSA) to perform singular spectrum analysis on each intrinsic mode function component. The resulting singular value spectrum matrices serve as input to a bidirectional long short-term memory (BiLSTM) neural network, which learns the feature representation and prediction model of the smart grid financial market through forward propagation and backpropagation.Results: The experimental results demonstrate that the proposed algorithm effectively predicts the smart grid financial market's time series, achieving high prediction accuracy and stability. The approach can contribute to sustainable innovation management and the development of the smart grid.Discussion: The VMD-SSA-BiLSTM algorithm's efficiency in extracting useful information from power grid signals and avoiding overfitting can improve the accuracy of timing predictions in the smart grid financial market. The algorithm's broad application prospects can promote sustainable innovation management and contribute to the development of the smart grid. |
| first_indexed | 2024-03-12T21:56:03Z |
| format | Article |
| id | doaj.art-660d091fa5b34477bc3b406286b0d446 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-12T21:56:03Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-660d091fa5b34477bc3b406286b0d4462023-07-25T17:03:57ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-07-011110.3389/fenrg.2023.12395421239542Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation managementChengran Yin0Guangming Wang1Jiacheng Liao2Krirk University, Bangkok, ThailandSchool of Management, Wuhan University of Technology, Wuhan, ChinaSchool of Economics and Management, Hubei Institute of Automobile Technology, Shiyan, ChinaIntroduction: This paper proposes a deep learning algorithm based on the VMD-SSA-BiLSTM model for time series forecasting in the smart grid financial market. The algorithm aims to extract useful information from power grid signals to improve the timing prediction accuracy and meet the needs of sustainable innovation management.Methods: The proposed algorithm employs the variational mode decomposition (VMD) method to decompose and reduce the dimensionality of historical data, followed by singular spectrum analysis (SSA) to perform singular spectrum analysis on each intrinsic mode function component. The resulting singular value spectrum matrices serve as input to a bidirectional long short-term memory (BiLSTM) neural network, which learns the feature representation and prediction model of the smart grid financial market through forward propagation and backpropagation.Results: The experimental results demonstrate that the proposed algorithm effectively predicts the smart grid financial market's time series, achieving high prediction accuracy and stability. The approach can contribute to sustainable innovation management and the development of the smart grid.Discussion: The VMD-SSA-BiLSTM algorithm's efficiency in extracting useful information from power grid signals and avoiding overfitting can improve the accuracy of timing predictions in the smart grid financial market. The algorithm's broad application prospects can promote sustainable innovation management and contribute to the development of the smart grid.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1239542/fullVMDSSABiLSTMsmart gridfinancial markettime series prediction |
| spellingShingle | Chengran Yin Guangming Wang Jiacheng Liao Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management Frontiers in Energy Research VMD SSA BiLSTM smart grid financial market time series prediction |
| title | Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management |
| title_full | Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management |
| title_fullStr | Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management |
| title_full_unstemmed | Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management |
| title_short | Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management |
| title_sort | application of vmd ssa bilstm algorithm to smart grid financial market time series forecasting and sustainable innovation management |
| topic | VMD SSA BiLSTM smart grid financial market time series prediction |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1239542/full |
| work_keys_str_mv | AT chengranyin applicationofvmdssabilstmalgorithmtosmartgridfinancialmarkettimeseriesforecastingandsustainableinnovationmanagement AT guangmingwang applicationofvmdssabilstmalgorithmtosmartgridfinancialmarkettimeseriesforecastingandsustainableinnovationmanagement AT jiachengliao applicationofvmdssabilstmalgorithmtosmartgridfinancialmarkettimeseriesforecastingandsustainableinnovationmanagement |