Improved deep mixed kernel randomized network for wind speed prediction

Abstract Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions. Although there are several intelligent techniques in the literature for wind speed prediction, their accuracies are not yet very reliable. Therefore, in this paper, a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder (AE) is proposed for wind speed prediction. The proposed method eliminates manual tuning of hidden nodes with random weights and biases, providing prediction model generalization and representation learning. This reduces reconstruction error due to the exact inversion of the kernel matrix, unlike the pseudo-inverse in a random vector functional-link network, and shortens the execution time. Furthermore, the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction accuracy. The kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization technique. The lowest errors in terms of mean absolute error (0.4139), mean absolute percentage error (4.0081), root mean square error (0.4843), standard deviation error (1.1431) and index of agreement (0.9733) prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs, deep kernel extreme learning machine AEs, deep kernel random vector functional-link network AEs, benchmark models such as least square support vector machine, autoregressive integrated moving average, extreme learning machines and their hybrid models along with different state-of-the-art methods.