Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model
The purpose of this study was to better apply artificial intelligence algorithm to load forecasting and effectively improve the forecasting accuracy. Based on the long short-term memory neural networks, a combined model based on whale bionic optimization is proposed for short-term load forecasting....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/2166082 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849305880898568192 |
|---|---|
| author | Lei Shao Quanjie Guo Chao Li Ji Li Huilong Yan |
| author_facet | Lei Shao Quanjie Guo Chao Li Ji Li Huilong Yan |
| author_sort | Lei Shao |
| collection | DOAJ |
| description | The purpose of this study was to better apply artificial intelligence algorithm to load forecasting and effectively improve the forecasting accuracy. Based on the long short-term memory neural networks, a combined model based on whale bionic optimization is proposed for short-term load forecasting. The whale bionic algorithm is used to solve the problem that the long short-term memory neural networks are easy to fall into local optimization and improve the accuracy of parameter optimization. The original signal is decomposed into multiple characteristic components by set empirical mode decomposition. Each feature component is input into the bionic optimized combination model for prediction. Finally, get the load forecasting results. Compared with the prediction results of EEMD-ARMA model, RNN model, LSTM model, and WOA-LSTM model, the combined prediction model optimized by whale bionics has less prediction error and higher prediction accuracy. |
| format | Article |
| id | doaj-art-2f92d732e16645ff94d3018299c0e411 |
| institution | Kabale University |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-2f92d732e16645ff94d3018299c0e4112025-08-20T03:55:16ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/2166082Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination ModelLei Shao0Quanjie Guo1Chao Li2Ji Li3Huilong Yan4School of Electrical Engineering and AutomationSchool of Electrical Engineering and AutomationSchool of Electrical Engineering and AutomationSchool of Electrical Engineering and AutomationSchool of Precision Instrument and Opto-Electronics EngineeringThe purpose of this study was to better apply artificial intelligence algorithm to load forecasting and effectively improve the forecasting accuracy. Based on the long short-term memory neural networks, a combined model based on whale bionic optimization is proposed for short-term load forecasting. The whale bionic algorithm is used to solve the problem that the long short-term memory neural networks are easy to fall into local optimization and improve the accuracy of parameter optimization. The original signal is decomposed into multiple characteristic components by set empirical mode decomposition. Each feature component is input into the bionic optimized combination model for prediction. Finally, get the load forecasting results. Compared with the prediction results of EEMD-ARMA model, RNN model, LSTM model, and WOA-LSTM model, the combined prediction model optimized by whale bionics has less prediction error and higher prediction accuracy.http://dx.doi.org/10.1155/2022/2166082 |
| spellingShingle | Lei Shao Quanjie Guo Chao Li Ji Li Huilong Yan Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model Applied Bionics and Biomechanics |
| title | Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model |
| title_full | Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model |
| title_fullStr | Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model |
| title_full_unstemmed | Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model |
| title_short | Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model |
| title_sort | short term load forecasting based on eemd woa lstm combination model |
| url | http://dx.doi.org/10.1155/2022/2166082 |
| work_keys_str_mv | AT leishao shorttermloadforecastingbasedoneemdwoalstmcombinationmodel AT quanjieguo shorttermloadforecastingbasedoneemdwoalstmcombinationmodel AT chaoli shorttermloadforecastingbasedoneemdwoalstmcombinationmodel AT jili shorttermloadforecastingbasedoneemdwoalstmcombinationmodel AT huilongyan shorttermloadforecastingbasedoneemdwoalstmcombinationmodel |