Short-Term Photovoltaic Power Forecasting Using a Bi-LSTM Neural Network Optimized by Hybrid Algorithms

Jibo Wang, Zihao Zhang, Wenhao Xu, Yijin Li () and Geng Niu
Additional contact information
Jibo Wang: School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China
Zihao Zhang: School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China
Wenhao Xu: School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China
Yijin Li: School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China
Geng Niu: State Grid Shanghai Energy Interconnection Research Institute, China Electric Power Research Institute, Haidian District, Beijing 100192, China

Sustainability, 2025, vol. 17, issue 12, 1-22

Abstract: Photovoltaic (PV) power generation is characterized by high fluctuation and intermittency. The accurate forecasting of PV power is crucial for optimizing grid operation and scheduling. Thus, a novel short-term PV power-forecasting method based on genetic algorithm-adaptive multi-objective differential evolution (GA-AMODE)-optimized bidirectional long short-term memory (BiLSTM) is proposed. Firstly, a data preprocessing method, including principal component analysis, a sliding window mechanism, and Gaussian noise injection, is designed to achieve dimension reduction and data robustness. Then, a GA-AMODE-BiLSTM model for PV power forecasting is proposed. GA and AMODE algorithms are integrated to balance global and local searching processes during the optimization of the BiLSTM network’s hyperparameters. Bi-LSTM is more suitable for complex time series tasks involving long-term dependencies and asymmetric relationships. The forecasting method is evaluated by typical indexes and is statistically tested. Comparative experiments using the same dataset across various models have been performed. The results show that the proposed GA-AMODE-BiLSTM model significantly outperforms other models in forecasting accuracy. Additionally, its superior stability and generalization is demonstrated, making the proposed method an effective tool for optimizing the management of renewable energy generation and enhancing the sustainability of energy systems.

Keywords: short-term photovoltaic power forecasting; GA-AMODE-BiLSTM model; data preprocessing; hyperparameter optimization; energy sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/12/5277/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/12/5277/ (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:jsusta:v:17:y:2025:i:12:p:5277-:d:1674030

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

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