Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short term wind speed
Abstract Short term wind speed forecasting has become an important technology to utilize sustainable energy, reduce the impact of wind power grid and improve the control of wind turbines. Current forecasting models based on individual algorithm and hybrid optimization algorithm could be applied to a variety of wind speed forecast. However, these algorithms ignored the limitation of optimizing parameter and the use of recent data, which may result in forecasting poor accuracy. In this paper, integrated approaches, combining singular spectrum analysis technique, cuckoo search algorithm (CSA) and harmony search algorithm (HSA) and back propagation neural network (BPNN) were introduced for conducting short term wind speed forecasting. Firstly, the SSA technique is used for identifying and extracting instable components from raw wind speed signals. Then, the parameters of BPNN are employed to be optimized by the CSA/HSA, which improve precision of forecast results. Finally, the BPNN is utilized to deal with the wind speed series. The proposed hybrid model is conducted by the wind speed at three stations located in Penglai, China. Our experiment reveals that the proposed hybrid model can generate a more accurate, reliable and robust result and SSA technique is found to be superior technique to preprocess wind speed series. Furthermore, we compare to the forecast results with SSA and non SSA denoising procedure and found that hybrid model with SSA technique outperforms other prediction model.
Neural Computing and Applications