The exploitation of thermal water and the mix of cold water changed the properties of geofluid in shallow reservoir,which altered the concentration of the chemical constitutes and continuously built new water-rock rea...The exploitation of thermal water and the mix of cold water changed the properties of geofluid in shallow reservoir,which altered the concentration of the chemical constitutes and continuously built new water-rock reaction. This paper deduced reservoir pressure and temperature variation tendency from 2004 to 2013,analyzed the change of some components in the shallow reservoir water,and finally obtained the evolution of the shallow geothermal water with hydrogeochemical model. The results show the reservoir pressure decreased significantly compared with the slight decline of reservoir temperature,and much cold groundwater infiltrated into the shallow reservoir,which affected the solubility of SiO2 and led to precipitation,the increased CO2 in shallow reservoir promoted the dissolution of aluminosilicate. Calcite and kaolinite precipitation zone has extended to the north in the field,which influenced the porosity of the reservoir rock.展开更多
This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance even...This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance events using a goodness of fit test and then pair the on-off events.Then the multi-dimensional features are extracted to establish a feature library.In the first stage identification,several groups of events for the appliance have been divided,according to three features,including phase,steady active power and power peak.In the second stage identification,a“one against the rest”support vector machine(SVM)model for each group is established to precisely identify the appliances.The proposed method is verified by using a public available dataset;the results show that the proposed method contains high generalization ability,less computation,and less training samples.展开更多
基金supported by the National Natural Science Foundation of China (No. 41572361)the China Geological Survey (No. 121201112006)
文摘The exploitation of thermal water and the mix of cold water changed the properties of geofluid in shallow reservoir,which altered the concentration of the chemical constitutes and continuously built new water-rock reaction. This paper deduced reservoir pressure and temperature variation tendency from 2004 to 2013,analyzed the change of some components in the shallow reservoir water,and finally obtained the evolution of the shallow geothermal water with hydrogeochemical model. The results show the reservoir pressure decreased significantly compared with the slight decline of reservoir temperature,and much cold groundwater infiltrated into the shallow reservoir,which affected the solubility of SiO2 and led to precipitation,the increased CO2 in shallow reservoir promoted the dissolution of aluminosilicate. Calcite and kaolinite precipitation zone has extended to the north in the field,which influenced the porosity of the reservoir rock.
基金supported by the National Science Foundation of China(U2166209,52007126)the Science and Technology Project of State Grid Tibet Electric Power Company(52311020009X)。
文摘This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance events using a goodness of fit test and then pair the on-off events.Then the multi-dimensional features are extracted to establish a feature library.In the first stage identification,several groups of events for the appliance have been divided,according to three features,including phase,steady active power and power peak.In the second stage identification,a“one against the rest”support vector machine(SVM)model for each group is established to precisely identify the appliances.The proposed method is verified by using a public available dataset;the results show that the proposed method contains high generalization ability,less computation,and less training samples.
