Research on Short-term Load Forecasting Algorithm Based on VMD and TCN
Aiming at the low accuracy of short-term load forecasting in substation area, a temporal convolutional network short-term load forecasting algorithm based on variational mode decomposition is proposed in this paper. It uses VMD to decompose load data to get a more regular subsequence, and uses th...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2024-04-01
|
| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2320 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Aiming at the low accuracy of short-term load forecasting in substation area, a temporal convolutional network short-term load forecasting algorithm based on variational mode decomposition is proposed in this paper. It uses VMD to decompose load data to get a more regular subsequence, and uses the maximum information coefficient to select weather factors strongly correlated with load, and forms a new load data set with the historical load and the subsequence of decomposition, using TCN model to complete short-term load forecasting in low-voltage substation areas. The prediction algorithms of TCN, LSTM and GRU are compared and analyzed. The simulation results show that the forecasting effect of VMD-TCN is the best, MAPE and RMSE are 1. 65% and 15. 05kW, respectively, indicating that the algorithm can be used to achieve accurate short-term forecasting of the station load, so as to facilitate the dispatch management, optimization operation, energy saving and emission reduction of the station. At the same time, another dataset was used to validate the algorithm, and the results showed that the forecasting results of VMD-TCN were still the best. |
|---|---|
| ISSN: | 1007-2683 |