Inevitable model uncertainty could render active yaw control (AYC) to make suboptimal or even irrational decisions. To this end, this paper quantitatively analyzes the influential mechanisms of uncertainty on optimal yaw policy in terms of the wake model and power model. In order to address power model uncertainty that has received little attention in the AYC field, this paper proposes an efficient adaptive economic predictive control. This framework begins with the construction of a neural network-based compensator to provide online adaptive estimation of parameter uncertainty. On this basis, an adaptive economic predictive control with the economic objective of maximizing the total power is designed to increase the confidence of yaw policy in the presence of generation uncertainty, and a parallel heuristic solver is developed to allow real-time control of large-scale wind farms. Finally, extensive results based on three wind farm cases demonstrate the effectiveness and advantages of the proposed control strategy.