This paper discusses the main impact factors of the local settlement and differential settlement of high- speed railway lines. The analysis results show that groundwater exploitation is the direct cause of differ- ent...This paper discusses the main impact factors of the local settlement and differential settlement of high- speed railway lines. The analysis results show that groundwater exploitation is the direct cause of differ- ential settlement. Based on the study of ballastless track additional load and of vehicle, track, and bridge dynamic responses under different differential settlements, a control standard of differential settlement during operation is proposed preliminarily.展开更多
This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future hi...This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.展开更多
基金supported by the National Nature Science Foundation of China (U1234206 and 61503311)+4 种基金support under the Railways Technology Development Plan of China Railway Corporation (2016X008-J)the Fundamental Research Funds for the Central Universities (2682015CX039)supported by the National United Engineering Laboratory of Integrated and Intelligent Transportation
文摘This paper discusses the main impact factors of the local settlement and differential settlement of high- speed railway lines. The analysis results show that groundwater exploitation is the direct cause of differ- ential settlement. Based on the study of ballastless track additional load and of vehicle, track, and bridge dynamic responses under different differential settlements, a control standard of differential settlement during operation is proposed preliminarily.
文摘This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.
