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Machine learning for energy-water nexus: challenges and opportunities

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Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. 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 interdependencies among these systems that can leave these systems vulnerable 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 conclude 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.
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Keywords: Machine learning; data; energy-water nexus

Document Type: Research Article

Affiliations: 1: Computer Science and Engineering Department, University at Buffalo, Buffalo, NY, USA 2: Computer Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA 3: Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

Publication date: July 3, 2018

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