Enhancing One-Day-Ahead Probabilistic Solar Power Forecast With a Hybrid Transformer-LUBE Model and Missing Data Imputation

Forecasting solar photovoltaic (PV) power generation has become important due to the chaotic and intermittent nature of solar power. With the development of artificial intelligence (AI) techniques, various deep learning algorithms have been shown to be very powerful for renewable power forecasts. Th...

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Veröffentlicht in:	IEEE transactions on industry applications 2024-01, Vol.60 (1), p.1-15
Hauptverfasser:	Phan, Quoc-Thang, Wu, Yuan-Kang, Phan, Quoc-Dung
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Artificial intelligence Artificial neural networks Correlation coefficients Curve fitting Data imputation Data models Data processing deep learning Error correction Forecasting lower upper bound estimation Machine learning Mathematical models Missing data Numerical models Numerical weather forecasting numerical weather prediction Photovoltaic cells post-processing Power pre-processing Predictive models probabilistic Probabilistic logic Solar energy solar power forecasting Solar power generation transformer Transformers Upper bounds Weather forecasting
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