Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of la...Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors.We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability,transport,and use to energy generation,fuel supply,and customer demand,and in the interde-pendencies among these systems that can leave these systems vul-nerable to cascading impacts from single disruptions.In this paper,we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves.We then survey machine learning techniques that have found application to date in energy-water nexus problems.We con-clude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.展开更多
The energy-water nexus,or the dependence of energy on water and water on energy,continues to receive attention as impacts on both energy and water supply and demand from growing popula-tions and climate-related stress...The energy-water nexus,or the dependence of energy on water and water on energy,continues to receive attention as impacts on both energy and water supply and demand from growing popula-tions and climate-related stresses are evaluated for future infra-structure planning.Changes in water and energy demand are related to changes in regional temperature,and precipitation extremes can affect water resources available for energy genera-tion for those regional populations.Additionally,the vulnerabilities to the energy and water nexus are beyond the physical infrastruc-tures themselves and extend into supporting and interdependent infrastructures.Evaluation of these vulnerabilities relies on the integration of the disparate and distributed data associated with each of the infrastructures,environments and populations served,and robust analytical methodologies of the data.A capability for the deployment of these methods on relevant data from multiple components on a single platform can provide actionable informa-tion for interested communities,not only for individual energy and water systems,but also for the system of systems that they com-prise.Here,we survey the highest priority data needs and analy-tical methods for inclusion on such a platform.展开更多
基金This manuscript has been authored by employees of UT- Battelle, under contract DE AC05-000R22725 with the US Department of Energy. The authors would also like to acknowledge thefinancial and intellectual support for this research by the Integrated Assessment Research Programof the US Department of Energy's Office of Science, Biological and Environmental Research. Thiswork is supported in part by NSF ACI-1541215.
文摘Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors.We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability,transport,and use to energy generation,fuel supply,and customer demand,and in the interde-pendencies among these systems that can leave these systems vul-nerable to cascading impacts from single disruptions.In this paper,we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves.We then survey machine learning techniques that have found application to date in energy-water nexus problems.We con-clude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.
基金This work was supported by the Integrated Assessment Research Program of the US Department of Energy’s Office of ScienceBiological and Environmental Research+1 种基金Department of Energy Office of PolicyNSF ACI-1541215.
文摘The energy-water nexus,or the dependence of energy on water and water on energy,continues to receive attention as impacts on both energy and water supply and demand from growing popula-tions and climate-related stresses are evaluated for future infra-structure planning.Changes in water and energy demand are related to changes in regional temperature,and precipitation extremes can affect water resources available for energy genera-tion for those regional populations.Additionally,the vulnerabilities to the energy and water nexus are beyond the physical infrastruc-tures themselves and extend into supporting and interdependent infrastructures.Evaluation of these vulnerabilities relies on the integration of the disparate and distributed data associated with each of the infrastructures,environments and populations served,and robust analytical methodologies of the data.A capability for the deployment of these methods on relevant data from multiple components on a single platform can provide actionable informa-tion for interested communities,not only for individual energy and water systems,but also for the system of systems that they com-prise.Here,we survey the highest priority data needs and analy-tical methods for inclusion on such a platform.
