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With the growing wind power penetration in the emerging power system, an accurate wind power forecasting method is very much essential, to help the system operators, to include wind generation into economic scheduling, unit commitment, and reserve allocation problems. It also assists the wind power producers to maximize their benefits by bidding in the electricity markets. A statistical-based wind power forecasting without using numerical weather prediction (NWP) inputs is carried out in this work. The proposed approach consists of two stages. In stage-I, wavelet decomposition of wind series is carried out and adaptive wavelet neural network (AWNN) is used to regress upon each decomposed signal, to predict wind speed up to 30 h ahead. In stage-II, a feed-forward neural network (FFNN) is used for nonlinear mapping between wind speed and wind power output, which transforms the forecasted wind speed into wind power prediction. The effectiveness of the proposed method is compared with persistence (PER) and new-reference (NR) benchmark models and the results show that the proposed model outperforms the benchmark models.