Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction. (15th January 2022) - Hua, Lei
Highlights: A novel hybrid approach is proposed for wind speed prediction. Simulated annealing and Latin hypercube sampling are used to improve ASO. The improved ASO algorithm is introduced to optimize the ELM network. VMD is used to decompose the original sequence into relative simple sub-modes. Nine benchmark models are used to verify the superiority of the proposed model. Abstract: Wind energy plays an important role in terms of renewable energy. Accurate and reliable wind speed prediction is essential for effective use of wind energy. However, the uncertainty of wind speed becomes a challenging task. Aiming at these problems of wind speed prediction, this paper proposes a method based on variational mode decomposition (VMD), partial least squares (PLS), improved atom search optimization (IASO) and extreme learning machine (ELM). VMD is first employed to decompose the original wind speed data from high frequency to low frequency into multiple sub-series. Then this paper uses PLS for feature extraction and gets the best test set. And IASO is used to optimize the ELM to enhance the performance of the basic ELM model. The simulated annealing algorithm is added to the atom search optimization to enhance the local searchability. This paper employs the original wind speed data of the Sotavento Galicia wind farm in Spain as case study. Comparing results between the proposed model and the other benchmark models demonstrate the superiority of the proposed model in short-term wind speed prediction.