A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm - Neshat, Mehdi; Nezhad, Meysam Majidi; Abbasnejad, Ehsan; Mirjalili, Seyedali; Tjernberg, Lina Bertling; Astiaso Garcia, Davide; Alexander, Bradley; Wagner, Markus (Energy conversion and management, 2021)
•A novel deep learning-based evolutionary model proposed for wind speed forecasting.•A new evolutionary hierarchy-based decomposition method introduced.•A developed evolutionary algorithm proposed in order to hyper-parameter tuning.•Generalised normal distribution algorithm is improved by an adaptive local search.•The proposed hybrid model improves the accuracy of short-term wind speed. Due to expanding global environmental issues and growing energy demand, wind power technologies have been studied extensively. Accurate and robust short-term wind speed forecasting is crucial for large-scale integration of wind power generation into the power grid. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction. This hybrid model consists of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria.