BN–LSSVM–BO model prediction of the impact of time-of-use electricity pricing strategies for electric vehicle charging demand

Electric vehicle charging behavior exhibits strong randomness, resulting in complex and uncertain factors influencing charging demand. To accurately characterize the multidimensional and dynamic variations of these factors, this research proposes a Bayesian network–least squares support vector machine (BN–LSSVM) ensemble model under Bayesian optimization (BO). This approach enables comprehensive multi-factor analysis while specifically highlighting the impact of time-of-use pricing on charging demand. First, considering the differences in charging demand among people on different dates and under different weather conditions, a multi-factor joint probability modeling—BN model was constructed to analyze the probability distribution parameters of charging demand under different combinations of factors. Subsequently, the LSSVM model is employed to integrate the probability characteristics of charging demand, constructing a nonlinear relationship between electricity prices and demand, and realizing the different effects of different strategies on charging demand. Furthermore, employing BO to optimize the regularization parameters and kernel parameters of LSSVM, thereby achieving hyperparameter adaptive global optimization. Finally, the article takes the historical data from the Honghai Technology Charging Station in Qilihe District, Lanzhou, over a 1-month period as a case study; the results show that the combined model BN–LSSVM–BO has the smallest mean absolute error of 1.66 and root mean square error of 2.76 compared to independent, unoptimized models, demonstrating better demand forecasting capabilities. The BN–LSSVM–BO peak-time floating strategy reduced peak-time demand by 29.4%, while the valley time discount increased valley time demand by 12.2%, verified the significant impact of time-of-use pricing strategies on charging demand.