Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine

• WABCRVM is presented for wind speed prediction.
• Wind speed is decomposed into four sub-signals with different frequency range.
• Establish the wavelet decomposed signals’ RVM models with each appropriate embedding dimension and kernel parameter.

In this paper, the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine (WABCRVM) is presented for wind speed prediction. Here, wind speed can be regarded as a signal and decomposed into four decomposed signals with different frequency range, which can be obtained by 2-layer wavelet decomposition for wind speed data, and the prediction models of the four decomposed signals can be established by RVM with their each appropriate embedding dimension. Artificial bee colony algorithm (ABC) is used to select the appropriate kernel parameters of their RVM models. Thus, each decomposed signal’s RVM model of wind speed has appropriate embedding dimension and kernel parameter. Finally, the experimental results show that it is feasible for the proposed combination scheme to improve the prediction ability of RVM for wind speed.