柔性负荷作为需求侧重要可调资源,能够参与电力系统灵活调节,促进新能源消纳。柔性负荷调控中存在的负荷功率属性、用户差异化舒适度以及状态序列等异构性问题,成为调控时需要考虑的难点。针对上述问题,该文首先利用预测平均指标(predic...柔性负荷作为需求侧重要可调资源,能够参与电力系统灵活调节,促进新能源消纳。柔性负荷调控中存在的负荷功率属性、用户差异化舒适度以及状态序列等异构性问题,成为调控时需要考虑的难点。针对上述问题,该文首先利用预测平均指标(predicted mean vote,PMV)舒适度指标综合量化用户舒适度,建立计及PMV的柔性负荷多功率级调节模型;然后根据聚类算法和PMV-PPD模型,提出计及多区域用户差异化PMV的消纳量分配策略;其次基于功率状态队列对单功率级和多功率级柔性负荷群开展联合调控策略,根据消纳量变化动态调节负荷功率。算例仿真表明所提策略相比传统方法能进一步实现精准消纳,显著提升用户舒适度,同时使多功率级负荷平稳运行。展开更多
The thermal comfort of passengers in the carriage cannot be ignored.Thus,this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal inpu...The thermal comfort of passengers in the carriage cannot be ignored.Thus,this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote(PMV)index.Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro.Support vector machine(SVM),decision tree(DT),random forest(RF),and logistic regression(LR)were used to build four models.This research aims to select the most appropriate input variables for the predictive model.All possible combinations of 11 input variables were used to determine the most accurate model,with variable selection for each model comprising 102350 iterations.In the PMV prediction,the RF model was the best when using the correlation coefficients square(R2)as the evaluation indicator(R^(2):0.7680,mean squared error(MSE):0.2868).The variables include clothing temperature(CT),convective heat transfer coefficient between the surface of the human body and the environment(CHTC),black bulb temperature(BBT),and thermal resistance of clothes(TROC).The RF model with MSE as the evaluation index also had the highest accuracy(R^(2):0.7676,MSE:0.2836).The variables include clothing surface area coefficient(CSAC),CT,BBT,and air velocity(AV).The results show that the RF model can efficiently predict the PMV of the subway car environment.展开更多
文摘柔性负荷作为需求侧重要可调资源,能够参与电力系统灵活调节,促进新能源消纳。柔性负荷调控中存在的负荷功率属性、用户差异化舒适度以及状态序列等异构性问题,成为调控时需要考虑的难点。针对上述问题,该文首先利用预测平均指标(predicted mean vote,PMV)舒适度指标综合量化用户舒适度,建立计及PMV的柔性负荷多功率级调节模型;然后根据聚类算法和PMV-PPD模型,提出计及多区域用户差异化PMV的消纳量分配策略;其次基于功率状态队列对单功率级和多功率级柔性负荷群开展联合调控策略,根据消纳量变化动态调节负荷功率。算例仿真表明所提策略相比传统方法能进一步实现精准消纳,显著提升用户舒适度,同时使多功率级负荷平稳运行。
文摘The thermal comfort of passengers in the carriage cannot be ignored.Thus,this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote(PMV)index.Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro.Support vector machine(SVM),decision tree(DT),random forest(RF),and logistic regression(LR)were used to build four models.This research aims to select the most appropriate input variables for the predictive model.All possible combinations of 11 input variables were used to determine the most accurate model,with variable selection for each model comprising 102350 iterations.In the PMV prediction,the RF model was the best when using the correlation coefficients square(R2)as the evaluation indicator(R^(2):0.7680,mean squared error(MSE):0.2868).The variables include clothing temperature(CT),convective heat transfer coefficient between the surface of the human body and the environment(CHTC),black bulb temperature(BBT),and thermal resistance of clothes(TROC).The RF model with MSE as the evaluation index also had the highest accuracy(R^(2):0.7676,MSE:0.2836).The variables include clothing surface area coefficient(CSAC),CT,BBT,and air velocity(AV).The results show that the RF model can efficiently predict the PMV of the subway car environment.
