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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Bibliographic Details
Published in:Energy (Oxford) Vol. 221; p. 119759
Main Authors: Meka, Rajitha, Alaeddini, Adel, Bhaganagar, Kiran
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 15.04.2021
Elsevier BV
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2021.119759