Short Term Wind Power Forecasting using Optimized WT-ANFIS Hybrid Model

The wind energy is the most encouraging and developed strategy among the sustainable energy source assets but its intermittency makes it difficult for schedule management. The wind power has been integrated into the electric grid due to its irregularity and uncertainty. Therefore, endeavors ought to be made in predicting the wind behavior and its relating electric generation. The wind power forecasting is required to cope with the stated challenges. For short term wind power forecasting, a hybrid intelligent technique is proposed in this paper. The proposed methodology is the hybridization of wavelet transform (WT) and particle swarm optimization (PSO) with adaptive-network-based fuzzy inference system (ANFIS). The average hourly time series data of one year has been taken from the wind park located at Agar in USA, which is first decomposed and then applied to the optimized ANFIS model using particle swarm optimization to predict the wind power output. The proposed hybrid Wavelet-Neuro-Fuzzy-PSO (WNFP) approaches as compared to other intelligent approaches show its superiority with respect to the wind power prediction.