Short-Term Wind Power Forecasting Using Interval A2-C1 Type-2 TSK FLS Method with Extended Kalman Filter Algorithm

For short-term wind power forecasting, an interval A2-C1 type-2 (IT2) Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS) method (“A” means antecedent and “C” consequent) based on an extended Kalman filter (EKF) optimization algorithm is proposed. Compared with the type-1 (T1) FLS model, the IT2 TSK FLS method can simultaneously model both intra- and inter-individual uncertainty and further optimize the antecedent and consequent parameters using the EKF to improve forecasting performance further. The proposed IT2 A2-C1 FLS method is applied to Mackey-Glass chaotic time series and wind power forecasting instances in a certain region, under the same conditions. It is also compared with the T1 TSK FLS and IT2 TSK FLS methods with back propagation (BP) and particle swarm optimization (PSO) algorithms, as well as IT2 A2-C0 TSK FLS methods with EKF. The experimental results confirm that the proposed IT2 A2-C1 FLS method is superior to the other FLS methods regarding performance, which demonstrates its effectiveness and application potential.