ArticleiScience2024
Dung beetle optimization algorithm-based hybrid deep learning model for ultra-short-term PV power prediction.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Improving the Dung Beetle Optimizer with Multiple Strategies: An Application to Complex Engineering Problems.Biomimetics (Basel, Switzerland) · 2025Article
- Adaptive singular spectral decomposition hybrid framework with quadratic error correction for wind power prediction.iScience · 2025Article
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Authors and funding
5 authors.
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Abstract
A hybrid model combining self-attention temporal convolutional networks (SATCN) with bidirectional long short-term memory (BiLSTM) networks was developed to improve the accuracy of ultra-short-term photovoltaic (PV) power prediction. The self-attention mechanism and SATCN were used to extract temporal and correlation features, which were then linked to BiLSTM networks. The model's hyperparameters were optimized using the dung beetle optimization algorithm. The model was tested on a year-long dataset of PV power and outperformed convolutional neural networks, BiLSTM networks, temporal convolutional networks, and other hybrid models. It reduced the root-mean-square error (RMSE) by 33.1% compared to the other models. The model achieved a mean absolute error (MAE) of 0.175, a weighted mean absolute percentage error (wMAPE) of 4.821, and a coefficient of determination (R2) of 0.997. These results highlight the model's superior accuracy and its potential applications in solar energy development.
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