Hybrid mechanism-data-driven method for energy consumption estimation model of shore bridge

Energy consumption acquisition of single equipment is of great significance to port energy consumption management. The existing methods for energy consumption acquisition of single equipment are complicated, cumbersome ， and not feasible. We propose a hybrid mechanism-data-driven energy consumption prediction method based on a support vector machine (SVM), which is selected by a whale optimization algorithm (WOA) on a hyperparameter. In this paper, the proposed algorithm is evaluated by using the measured energy consumption data of the shore bridge, and two comparison experiments are designed. The results indicate that the proposed WOA-SVM-based hybrid mechanism-data-driven energy consumption model can be used for shore bridge energy consumption prediction and has higher prediction accuracy compared with other prediction methods.