An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm

Understand

Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation.

• However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task.
• This study uses a new hybrid evolutionary approach that uses a popular evolutionary search algorithm, CMA-ES, to tune the hyper-parameters of two Long short-term memory(LSTM) ANN models for wind prediction.
• The proposed hybrid approach is trained on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea.