Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction

Accurate wind speed prediction is essential for proper use of wind energy resource. In this paper, a novel hybrid multi-step wind speed forecasting model is developed, which consists of sparse feature extraction, bidirectional deep learning, multi-objective optimization, and adaptive decomposition-based error correction. Apart from the traditional average-based resolution transformation method, a two-layer stacked sparse autoencoder (SSAE) is proposed to extract the hidden representation of original 3 s high-resolution wind speed data. Trained by the data generated from different resolution transformation methods, two bidirectional long short-term memory (BiLSTM) networks serve as base predictors and provide 10-step forecasting results. The results of base predictors are reasonably ensembled by multi-objective multi-universe optimization (MOMVO). Moreover, to reduce the predictable components in error series further, a correction model based on empirical wavelet transform (EWT) and outlier robust extreme learning machine model (ORELM) is constructed to reduce the forecasting error further. The effectiveness of the proposed hybrid model is comprehensively evaluated by a series of experiments. The experimental results demonstrate that: (a) the proposed model is well trained, with great convergence, and an average RMSE of 0.2618 m/s in 10-step forecasting; (b) the proposed model outperforms other existing models in all experimental sites and forecasting steps; (c) the multi-objective optimization algorithm can rationally integrate base predictors to obtain better performance in each step; (d) the proposed residual error correction model can generate more than 78% improvement of RMSE, significantly better than compared correction methods.