A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables

Short-term (less than 1 h) forecast of the power generated by wind turbines in a wind farm is extremely challenging due to the lack of reliable data from meteorological towers and numerical weather model outputs at these timescales. A robust deep learning model is developed for short-term forecasts...

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Vydáno v:	Energy (Oxford) Ročník 221; s. 119759
Hlavní autoři:	Meka, Rajitha, Alaeddini, Adel, Bhaganagar, Kiran
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 15.04.2021 Elsevier BV
Témata:	Deep learning Design of experiments Design optimization energy experimental design Mathematical models Neural networks Orthogonal array tuning method (OATM) Orthogonal arrays Robustness (mathematics) Short-term prediction Taguchi methods Temporal convolutional network (TCN) temporal variation Turbines Weather wind Wind farm power prediction Wind farms Wind power Wind speed Wind turbine Wind turbines Short-term prediction Neural networks Wind farm power prediction Wind turbine Temporal convolutional network (TCN) Orthogonal array tuning method (OATM)
ISSN:	0360-5442, 1873-6785
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Short-term (less than 1 h) forecast of the power generated by wind turbines in a wind farm is extremely challenging due to the lack of reliable data from meteorological towers and numerical weather model outputs at these timescales. A robust deep learning model is developed for short-term forecasts of wind turbine generated power in a wind farm using the state-of-the-art temporal convolutional networks (TCN) to simultaneously capture the temporal dynamics of the wind turbine power and relationship among the local meteorological variables. An orthogonal array tuning method based on the Taguchi design of experiments is utilized to optimize the hyperparameters of the proposed TCN model. The proposed TCN model is validated using twelve months of data from a 130 MW utility-scale wind farm with 86 wind turbines in comparison with some of the existing methods in the literature. The power curves obtained from the proposed TCN model show consistent improvements over existing methods at all wind speeds. •Deep learning model based on state-of-the-art TCN to predict the total wind power.•Multi-step prediction of wind power for 0, 10, 20, 30, 40 and 50 min ahead.•OATM to optimize the hyperparameters of the deep learning models.•Comparison of multi-step ahead TCN model against LSTM, CNN + LSTM and MLR models.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0360-5442 1873-6785
DOI:	10.1016/j.energy.2021.119759