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Accurate short-term wind speed forecasting is essential for large-scaleintegration of wind power generation. However, the seasonal and stochasticcharacteristics of wind speed make forecasting a challenging task. This studyuses a new hybrid evolutionary approach that uses a popular evolutionary searchalgorithm, CMA-ES, to tune the hyper-parameters of two Long short-termmemory(LSTM) ANN models for wind prediction. The proposed hybrid approach istrained on data gathered from an offshore wind turbine installed in a Swedishwind farm located in the Baltic Sea. Two forecasting horizons includingten-minutes ahead (absolute short term) and one-hour ahead (short term) areconsidered in our experiments. Our experimental results indicate that the newapproach is superior to five other applied machine learning models, i.e.,polynomial neural network (PNN), feed-forward neural network (FNN), nonlinearautoregressive neural network (NAR) and adaptive neuro-fuzzy inference system(ANFIS), as measured by five performance criteria.

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