EconPapers    
Economics at your fingertips  
 

Short-Term Probabilistic Prediction of Photovoltaic Power Based on Bidirectional Long Short-Term Memory with Temporal Convolutional Network

Weibo Yuan (), Jinjin Ding, Li Zhang, Jingyi Ni and Qian Zhang
Additional contact information
Weibo Yuan: State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China
Jinjin Ding: State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China
Li Zhang: State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China
Jingyi Ni: State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China
Qian Zhang: School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China

Energies, 2025, vol. 18, issue 20, 1-17

Abstract: To mitigate the impact of photovoltaic (PV) power generation uncertainty on power systems and accurately depict the PV output range, this paper proposes a quantile regression probabilistic prediction model (TCN-QRBiLSTM) integrating a Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM). First, the historical dataset is divided into three weather scenarios (sunny, cloudy, and rainy) to generate training and test samples under the same weather conditions. Second, a TCN is used to extract local temporal features, and BiLSTM captures the bidirectional temporal dependencies between power and meteorological data. To address the non-differentiable issue of traditional interval prediction quantile loss functions, the Huber norm is introduced as an approximate replacement for the original loss function by constructing a differentiable improved Quantile Regression (QR) model to generate confidence intervals. Finally, Kernel Density Estimation (KDE) is integrated to output probability density prediction results. Taking a distributed PV power station in East China as the research object, using data from July to September 2022 (15 min resolution, 4128 samples), comparative verification with TCN-QRLSTM and QRBiLSTM models shows that under a 90% confidence level, the Prediction Interval Coverage Probability (PICP) of the proposed model under sunny/cloudy/rainy weather reaches 0.9901, 0.9553, 0.9674, respectively, which is 0.56–3.85% higher than that of comparative models; the Percentage Interval Normalized Average Width (PINAW) is 0.1432, 0.1364, 0.1246, respectively, which is 1.35–6.49% lower than that of comparative models; the comprehensive interval evaluation index (I) is the smallest; and the Bayesian Information Criterion (BIC) is the lowest under all three weather conditions. The results demonstrate that the model can effectively quantify and mitigate PV power generation uncertainty, verifying its reliability and superiority in short-term PV power probabilistic prediction, and it has practical significance for ensuring the safe and economical operation of power grids with high PV penetration.

Keywords: photovoltaic; probability prediction; TCN; quantile regression; BiLSTM (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: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/20/5373/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/20/5373/ (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:18:y:2025:i:20:p:5373-:d:1769540

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

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

 
Page updated 2025-10-15
Handle: RePEc:gam:jeners:v:18:y:2025:i:20:p:5373-:d:1769540