Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMD
In order to solve the difficulties that the uncertain characteristics of PV output, such as volatility and intermittency, will bring to the development of microgrid scheduling plans, this paper proposes a Transformer–Bidirectional Long Short-Term Memory (BiLSTM) neural network PV power generation fo...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/24/11991 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846105898577035264 |
|---|---|
| author | Xiaowei Fan Ruimiao Wang Yi Yang Jingang Wang |
| author_facet | Xiaowei Fan Ruimiao Wang Yi Yang Jingang Wang |
| author_sort | Xiaowei Fan |
| collection | DOAJ |
| description | In order to solve the difficulties that the uncertain characteristics of PV output, such as volatility and intermittency, will bring to the development of microgrid scheduling plans, this paper proposes a Transformer–Bidirectional Long Short-Term Memory (BiLSTM) neural network PV power generation forecasting fusion model based on the Newton–Raphson optimization algorithm (NRBO) and Variational Modal Decomposition (VMD). Firstly, the principle of the VMD technique and the Gray Wolf Optimization (GWO) algorithm’s key parameter optimization method for VMD are introduced. Then, the Transformer decoder partially fuses the BiLSTM network and retains the encoder to obtain the body of the prediction model, followed by explaining the principle of the NRBO algorithm. And finally, the VMD-NRBO-Transformer-BiLSTM prediction model and hyperparameter selection are evaluated by the NRBO algorithm. The algorithm sets up a multi-model comparison experiment, and the results show that the prediction model proposed in this paper has the best prediction accuracy and the optimal evaluation index. |
| format | Article |
| id | doaj-art-0f6b306a558a4a3d90ff73b125d90c54 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0f6b306a558a4a3d90ff73b125d90c542024-12-27T14:09:02ZengMDPI AGApplied Sciences2076-34172024-12-0114241199110.3390/app142411991Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMDXiaowei Fan0Ruimiao Wang1Yi Yang2Jingang Wang3State Grid Chongqing Electric Power Company, Chongqing 400014, ChinaState Grid Chongqing Electric Power Company Electric Power Science Research Institute, Chongqing 401123, ChinaState Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaState Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaIn order to solve the difficulties that the uncertain characteristics of PV output, such as volatility and intermittency, will bring to the development of microgrid scheduling plans, this paper proposes a Transformer–Bidirectional Long Short-Term Memory (BiLSTM) neural network PV power generation forecasting fusion model based on the Newton–Raphson optimization algorithm (NRBO) and Variational Modal Decomposition (VMD). Firstly, the principle of the VMD technique and the Gray Wolf Optimization (GWO) algorithm’s key parameter optimization method for VMD are introduced. Then, the Transformer decoder partially fuses the BiLSTM network and retains the encoder to obtain the body of the prediction model, followed by explaining the principle of the NRBO algorithm. And finally, the VMD-NRBO-Transformer-BiLSTM prediction model and hyperparameter selection are evaluated by the NRBO algorithm. The algorithm sets up a multi-model comparison experiment, and the results show that the prediction model proposed in this paper has the best prediction accuracy and the optimal evaluation index.https://www.mdpi.com/2076-3417/14/24/11991PVforecasting modelNRBOVMDtransformer-BiLSTM |
| spellingShingle | Xiaowei Fan Ruimiao Wang Yi Yang Jingang Wang Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMD Applied Sciences PV forecasting model NRBO VMD transformer-BiLSTM |
| title | Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMD |
| title_full | Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMD |
| title_fullStr | Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMD |
| title_full_unstemmed | Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMD |
| title_short | Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMD |
| title_sort | transformer bilstm fusion neural network for short term pv output prediction based on nrbo algorithm and vmd |
| topic | PV forecasting model NRBO VMD transformer-BiLSTM |
| url | https://www.mdpi.com/2076-3417/14/24/11991 |
| work_keys_str_mv | AT xiaoweifan transformerbilstmfusionneuralnetworkforshorttermpvoutputpredictionbasedonnrboalgorithmandvmd AT ruimiaowang transformerbilstmfusionneuralnetworkforshorttermpvoutputpredictionbasedonnrboalgorithmandvmd AT yiyang transformerbilstmfusionneuralnetworkforshorttermpvoutputpredictionbasedonnrboalgorithmandvmd AT jingangwang transformerbilstmfusionneuralnetworkforshorttermpvoutputpredictionbasedonnrboalgorithmandvmd |