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Wind speed forecasting has an unsuperseded function in the high-efficiency operation of wind farms, and is significant in wind-related engineering studies. Back-propagation (BP) algorithms have been comprehensively employed to forecast time series that are nonlinear, irregular, and unstable. However, the single model usually overlooks the importance of data pre-processing and parameter optimization of the model, which results in weak forecasting performance. In this paper, a more precise and robust model that combines data pre-processing, BP neural network, and a modified artificial intelligence optimization algorithm was proposed, which succeeded in avoiding the limitations of the individual algorithm. The novel model not only improves the forecasting accuracy but also retains the advantages of the firefly algorithm (FA) and overcomes the disadvantage of the FA while optimizing in the later stage. To verify the forecasting performance of the presented hybrid model, 10-min wind speed data from Penglai city, Shandong province, China, were analyzed in this study. The simulations revealed that the proposed hybrid model significantly outperforms other single metaheuristics

An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting