Day-ahead photovoltaic power forecasting with multi-source temporal-feature convolutional networks

Day-ahead photovoltaic power forecasting with multi-source temporal-feature convolutional networks

Abstract Photovoltaic (PV) power forecasting technology enhances the absorption capacity of renewable energy. However, the PV power generation process is highly sensitive to fluctuations in weather conditions, making accurate forecasting challenging. In this paper, we propose a composite data augmen...

Full description

Saved in:
Bibliographic Details
Main Authors: Ziming Ouyang, Zhaohui Li, Xiangdong Chen
Format: Article
Language:English
Published: SpringerOpen 2025-05-01
Series:Energy Informatics
Subjects:
Photovoltaic power forecasting
Data augmentation
Convolutional neural network
Channel attention mechanism
Feature extraction
Time series analysis
Online Access:https://doi.org/10.1186/s42162-025-00531-7
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s42162-025-00531-7

Similar Items