Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion
ABSTRACT The accurate prediction of the output power of each power plant is crucial for effective resource deployment. This paper proposes a convolutional neural network‐long short‐term memory (CNN‐LSTM) network integration model based on spatio‐temporal feature fusion. Firstly, the temporal correla...
Saved in:
Main Authors: | Junwei Ma, Meiru Huo, Jinfeng Han, Yunfeng Liu, Shunfa Lu, Xiaokun Yu |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2025-01-01
|
Series: | Engineering Reports |
Subjects: | |
Online Access: | https://doi.org/10.1002/eng2.13088 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System
by: Jiahui Cheng, et al.
Published: (2024-01-01) -
A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships
by: Babak Amiri, et al.
Published: (2025-01-01) -
Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
by: Yonghao He, et al.
Published: (2025-01-01) -
Interpretable DWT-1DCNN-LSTM Network for Power Quality Disturbance Classification
by: Shuangquan Yang, et al.
Published: (2025-01-01) -
Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network
by: PAN Dong, YANG Xin, SHI Tiancheng, FANG Yuan, WANG Xuli, DOU Menghan
Published: (2025-01-01)