China＇s energy supply-and-demand model and two related carbon emission scenarios, including a planned peak scenario and an advanced peak scenario, are designed taking into consideration China＇s economic development,...China＇s energy supply-and-demand model and two related carbon emission scenarios, including a planned peak scenario and an advanced peak scenario, are designed taking into consideration China＇s economic development, technological progress, policies, resources, environmental capacity, and other factors. The analysis of the defined scenarios provides the following conclusions： Primary energy and power demand will continue to grow leading up to 2030, and the growth rate of power demand will be much higher than that of primary energy demand. Moreover, low carbonization will be a basic feature of energy supply-and-demand structural changes, and non-fossil energy will replace oil as the second largest energy source. Finally, energy- related carbon emissions could peak in 2025 through the application of more efficient energy consumption patterns and more low-carbon energy supply modes. The push toward decarbonization of the power industry is essential for reducing the peak value of carbon emissions.展开更多
This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand...This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.展开更多
Accurate flood prediction is an important tool for risk management and hydraulic works design on a watershed scale. The objective of this study was to calibrate and validate 24 linear and non-linear regression models,...Accurate flood prediction is an important tool for risk management and hydraulic works design on a watershed scale. The objective of this study was to calibrate and validate 24 linear and non-linear regression models, using only upstream data to estimate real-time downstream flooding. Four critical downstream estimation points in the Mataquito and Maule river basins located in central Chile were selected to estimate peak flows using data from one, two, or three upstream stations. More than one thousand paper-based storm hydrographs were manually analyzed for rainfall events that occurred between 1999 and 2006, in order to determine the best models for predicting downstream peak flow. The Peak Flow Index (IQP) (defined as the quotient between upstream and downstream data) and the Transit Times (TT) between upstream and downstream points were also obtained and analyzed for each river basin. The Coefficients of Determination (R2), the Standard Error of the Estimate (SEE), and the Bland-Altman test (ACBA) were used to calibrate and validate the best selected model at each basin. Despite the high variability observed in peak flow data, the developed models were able to accurately estimate downstream peak flows using only upstream flow data.展开更多
文摘China＇s energy supply-and-demand model and two related carbon emission scenarios, including a planned peak scenario and an advanced peak scenario, are designed taking into consideration China＇s economic development, technological progress, policies, resources, environmental capacity, and other factors. The analysis of the defined scenarios provides the following conclusions： Primary energy and power demand will continue to grow leading up to 2030, and the growth rate of power demand will be much higher than that of primary energy demand. Moreover, low carbonization will be a basic feature of energy supply-and-demand structural changes, and non-fossil energy will replace oil as the second largest energy source. Finally, energy- related carbon emissions could peak in 2025 through the application of more efficient energy consumption patterns and more low-carbon energy supply modes. The push toward decarbonization of the power industry is essential for reducing the peak value of carbon emissions.
文摘This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.
文摘Accurate flood prediction is an important tool for risk management and hydraulic works design on a watershed scale. The objective of this study was to calibrate and validate 24 linear and non-linear regression models, using only upstream data to estimate real-time downstream flooding. Four critical downstream estimation points in the Mataquito and Maule river basins located in central Chile were selected to estimate peak flows using data from one, two, or three upstream stations. More than one thousand paper-based storm hydrographs were manually analyzed for rainfall events that occurred between 1999 and 2006, in order to determine the best models for predicting downstream peak flow. The Peak Flow Index (IQP) (defined as the quotient between upstream and downstream data) and the Transit Times (TT) between upstream and downstream points were also obtained and analyzed for each river basin. The Coefficients of Determination (R2), the Standard Error of the Estimate (SEE), and the Bland-Altman test (ACBA) were used to calibrate and validate the best selected model at each basin. Despite the high variability observed in peak flow data, the developed models were able to accurately estimate downstream peak flows using only upstream flow data.
