Short-Term Power Load Prediction Based on Level Processing Method and Improved GWO Algorithm
In the field of short-term power load prediction, the current prediction methods have low prediction accuracy. To address this issue, this study introduces level processing method and improved grey wolf genetic algorithm to predict short-term power load and optimize the power load prediction accurac...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10979938/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In the field of short-term power load prediction, the current prediction methods have low prediction accuracy. To address this issue, this study introduces level processing method and improved grey wolf genetic algorithm to predict short-term power load and optimize the power load prediction accuracy. The genetic algorithm is applied to optimize the traditional grey wolf algorithm. Then, combined with the level set algorithm in the level processing algorithm, a genetic grey wolf hybrid model that integrates level processing is constructed. The variables in the load data are processed and analyzed through the level set algorithm. The final position of the population is determined based on the improved grey wolf genetic algorithm. Comparative experiments are conducted among the proposed model, the long short-term memory model, as well as the variational mode decomposition model. The average prediction accuracy remained within 0.652-0.859, significantly higher than the other two comparative models. The mean absolute error was 1.869, significantly lower than the other two models. The F1 score and accuracy were 0.891 and 90.32%, demonstrating that its predictive performance was significantly better than the other two models. Precision-recall curve, accuracy, mean absolute error, F1 score and other indicators are applied to evaluate the performance of the three models. The proposed model can accurately perform load prediction analysis in short-term power load prediction, and its prediction performance exceeds the other two prediction models. The prediction method can accurately predict short-term power load, providing useful references and inspirations for future researchers in power load prediction, and promoting the continuous development and progress of short-term power load prediction technology. |
|---|---|
| ISSN: | 2169-3536 |