Power Forecasting for Photovoltaic Microgrid Based on MultiScale CNN-LSTM Network Models

Honglin Xue, Junwei Ma, Jianliang Zhang, Penghui Jin, Jian Wu and Feng Du ()
Additional contact information
Honglin Xue: Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China
Junwei Ma: Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China
Jianliang Zhang: Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China
Penghui Jin: State Grid Block Chain Technology (Beijing) Co., Ltd., Beijing 100053, China
Jian Wu: Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China
Feng Du: Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China

Energies, 2024, vol. 17, issue 16, 1-13

Abstract: Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture for short-term prediction of PV power. Firstly, in order to make full use of the spatial information between different power stations, a spatio–temporal feature fusion method is proposed. This method is capable of exploiting both the power information of neighboring power stations with strong correlations and meteorological information with the PV feature data of the target power station. By using a multiscale convolutional neural network–long short-term memory (CNN-LSTM) network model, it is capable of generating a PV feature dataset containing spatio–temporal attributes that expand the data source and enhance the feature constraints. It is capable of predicting the output power sequences of power stations in PV microgrids with high model generalization and responsiveness. To validate the effectiveness of the proposed framework, an extensive numerical analysis is also conducted based on a real-world PV dataset.

Keywords: photovoltaic power prediction; convolutional neural network; long short-term memory network; multiscale (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/16/3877/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/16/3877/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:16:p:3877-:d:1450990

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().