Interval prediction of short‐term photovoltaic power based on an improved GRU model

The accurate prediction of photovoltaic (PV) power is crucial for planning, constructing, and scheduling high‐penetration distributed PV power systems. Traditional point prediction methods suffer from instability and lack reliability, which can be effectively addressed through interval prediction. T...

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Bibliographic Details
Published in:Energy science & engineering 2024-07, Vol.12 (7), p.3142-3156
Main Authors: Zhang, Jing, Liao, Zhuoying, Shu, Jie, Yue, Jingpeng, Liu, Zhenguo, Tao, Ran
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Artificial neural networks
Clustering
coverage width‐based criterion
improved gate recurrent unit
interval prediction
Methods
Neural networks
Optimization algorithms
Permutations
Photovoltaic cells
Photovoltaics
Quantiles
R&D
Research & development
Search algorithms
short‐term photovoltaic power prediction
Solar radiation
System reliability
Systems stability
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Description
Summary:The accurate prediction of photovoltaic (PV) power is crucial for planning, constructing, and scheduling high‐penetration distributed PV power systems. Traditional point prediction methods suffer from instability and lack reliability, which can be effectively addressed through interval prediction. This study proposes a short‐term PV power interval prediction method based on the framework of sparrow search algorithm (SSA)‐variational mode decomposition (VMD)‐convolutional neural network (CNN)‐gate recurrent unit (GRU). First, PV data undergo similar day clustering based on permutation entropy and VMD is applied to solar radiation signals with high correlation. Then, the hyperparameters of GRU are optimized by SSA according to the comprehensive evaluation indicator of interval prediction proposed in this study. Subsequently, quantile prediction results are obtained based on CNN‐GRU using the optimal parameters from SSA optimization. Finally, the prediction interval is composed of multiple quantile prediction results. A MATLAB R2022b program is developed to compare different prediction methods. The results demonstrate that compared to single neural network methods, the proposed method effectively improves the coverage width‐based criterion. In the interval prediction of sunny and rainy similar days, the comprehensive evaluation indicators of the proposed method are only 54.3% and 37.4% of the single GRU, respectively, indicating significantly improved interval prediction accuracy. Gate recurrent unit (GRU) network structure.
ISSN:2050-0505
2050-0505
DOI:10.1002/ese3.1811