TY - JOUR
T1 - Wind power forecasting – A data-driven method along with gated recurrent neural network
AU - Kisvari, Adam
AU - Lin, Zi
AU - Liu, Xiaolei
PY - 2021/1
Y1 - 2021/1
N2 - Effective wind power prediction will facilitate the world’s long-term goal in sustainable development. However, a drawback of wind as an energy source lies in its high variability, resulting in a challenging study in wind power forecasting. To solve this issue, a novel data-driven approach is proposed for wind power forecasting by integrating data pre-processing & re-sampling, anomalies detection & treatment, feature engineering, and hyperparameter tuning based on gated recurrent deep learning models, which is systematically presented for the first time. Besides, a novel deep learning neural network of Gated Recurrent Unit (GRU) is successfully developed and critically compared with the algorithm of Long Short-term Memory (LSTM). Initially, twelve features were engineered into the predictive model, which are wind speeds at four different heights, generator temperature, and gearbox temperature. The simulation results showed that, in terms of wind power forecasting, the proposed approach can capture a high degree of accuracy at lower computational costs. It can also be concluded that GRU outperformed LSTM in predictive accuracy under all observed tests, which provided faster training process and less sensitivity to noise in the used Supervisory Control and Data Acquisition (SCADA) datasets.
AB - Effective wind power prediction will facilitate the world’s long-term goal in sustainable development. However, a drawback of wind as an energy source lies in its high variability, resulting in a challenging study in wind power forecasting. To solve this issue, a novel data-driven approach is proposed for wind power forecasting by integrating data pre-processing & re-sampling, anomalies detection & treatment, feature engineering, and hyperparameter tuning based on gated recurrent deep learning models, which is systematically presented for the first time. Besides, a novel deep learning neural network of Gated Recurrent Unit (GRU) is successfully developed and critically compared with the algorithm of Long Short-term Memory (LSTM). Initially, twelve features were engineered into the predictive model, which are wind speeds at four different heights, generator temperature, and gearbox temperature. The simulation results showed that, in terms of wind power forecasting, the proposed approach can capture a high degree of accuracy at lower computational costs. It can also be concluded that GRU outperformed LSTM in predictive accuracy under all observed tests, which provided faster training process and less sensitivity to noise in the used Supervisory Control and Data Acquisition (SCADA) datasets.
KW - Wind power forecasting
KW - SCADA data
KW - Feature engineering
KW - Deep learning
KW - Offshore wind turbines
U2 - 10.1016/j.renene.2020.10.119
DO - 10.1016/j.renene.2020.10.119
M3 - Article
VL - 163
SP - 1895
EP - 1909
JO - Renewable Energy
JF - Renewable Energy
SN - 0960-1481
ER -