The existing charging piles planning method of distribution network does not consider the influence of user participation in regulation. Hence, an optimal planning method of electric vehicle charging piles in V2G mode is proposed, considering users’ influence of regulation. An EV charging pile planning model is established to minimize the investment and operation cost of distribution network and charging station, and the genetic algorithm with elitist retention strategy is used to solve the model. The examples show the effectiveness of the proposed method.
Aiming at the current low pre-diabetes detection rate, this paper proposes a PSO-SVM model to assist doctors in identifying the risk of patients with pre-diabetes. The paper uses the Support Vector Machine as the verification algorithm, takes the radial basis kernel as the kernel function, uses the adaptive Particle Swarm Optimization algorithm to optimize the penalty factor and kernel parameters of the Support Vector Machine, and establishes a PSO-SVM model, finally compares the model with Neural Network, Logistic Regression, and Naive Bayes model, and use Sensitivity, Specificity indicators and ROC curve to evaluate model performance. Empirical analysis proves that the combined model proposed in this paper can effectively identify the risk of patients with prediabetes.
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