Efficient power generation and reduced operational cost are crucial to make wind energy a successful market player. To achieve satisfactory performance in wind turbine technology, it is essential to accurately predict the rotor equivalent wind speed (REWS). Since traditional wind transducers are difficult to measure multiple-point wind speed, related researches on predicting REWS are lacking. In this paper, a hybrid REWS prediction framework is proposed, which combines mechanism analysis and data-driven technique, aided by Lidar measurements. The framework employs the mechanism modeling and time delay analysis to achieve preliminary REWS prediction, but the accuracy is restricted by the quantity of Lidar measurement points. To enhance the prediction accuracy, a residual prediction model based on the data-driven technique is created to correct the mechanism prediction. Specifically, a novel combined modeling approach based on empirical mode decomposition and sample entropy algorithms is presented. Through reconstructing the input data of the combined model into high, middle, and low frequency components and exploring the prediction laws of different frequency components obtained by typical machine learning models, the combined residual prediction model is established. Additionally, an enhanced whale optimization algorithm named WOAmM is presented to optimize the parameters of the combined model. The proposed method is validated through a four-beam Lidar measurement scenario simulated by BLADED software. The experimental results indicate that the combined model outperforms each single model and the mechanism prediction, and improves the REWS prediction accuracy by up to75.04%, 60.92%, 51.85%, and 57.52% in the four datasets, respectively. The proposed method could facilitate the utilization of Lidar in improving the performance of wind turbines.