An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction

Short-term wind power forecasting using interval A2-C1 type-2 TSK FLS method with extended Kalman filter algorithm

“…Step 6: The explicit dependence of l y and r y on the membership function is determined through Equation (5). From equations (10) and (11), it can be concluded that:…”

“…( 38)-( 61). The H matrices corresponding to the parameter vector sets are given as: H 11 , H 12 , H 13 for the antecedent parameters and H…”

“…(35). Update the Kalman gains K 11 , K 12 , K 13 and K 21 , K 22 . Finally, update the covariance matrices P 11 ， P 12 , P 13 and P 21 In the test phase, the forecasting inputs are applied to the trained model based on A2-C1 TSK FLS, then the corresponding predicted output of the model are obtained.…”

“…Therefore the instability of wind power generation has become an important obstacle to the effective utilization and management of wind energy [9,10] . Moreover, since the primary objective of wind farm owners is to maximize profits in the electricity market, accurate wind power forecasts play a crucial role in power system planning, operation and management [11] . The current research on wind power forecasting methods can be categorized into ultra-short-term forecasting, short-term forecasting, medium-term forecasting and long-term forecasting according to the forecasting time scale [12,13] .…”