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Gerard Rendell    (1)

Lead Business and Technical consultant in Market Solutions Analytics, an advanced analytics incubator and solution development provider within Duke Energy, that focuses on using data science and machine learning to build a smarter energy future. Working on market research and strategy for transformational solutions across the generation, transmission, distribution and customer landscape. Interested in the intersection of design thinking, computational economics, abstract algebra and the use of machine learning with ontologies to solve strategic problems. Gerard gained a Ph.D. from University of London (Kingston in 2012) with concentrations in computing and mathematics with a thesis on evolving machines with human like characteristics of early childhood mathematics development using cognitive models from Jean Piaget. Latest project is working with SEPA to develop a Transactive Energy Ontology for the electric grid using OWL, UML and Python.     (1A)

recent projects @ Duke-Energy     (1B)

• Working with SEPA to develop a Transactive Energy Ontology for the electric grid using OWL, UML and Python.     (1C)

• With a team of three developed the Portfolio Pipeline for Market Solutions Analytics which valued the advanced analytics solutions business unit at $70M+.     (1D)

• Created a scenario planning tool with non-linear optimization to select optimal solution portfolio for descriptive, diagnostic, predictive, prescriptive and cognitive analytic products.     (1E)

• Crafted the strategy for Market Solutions Analytics and its portfolio pipeline and associated product strategies.     (1F)

• Developed an integrated electric utility business model (14 core processes, 300+ data and synchronization points) in Enterprise Architect, which is used for the selection of utility solutions.     (1G)

• Defined the structure and function of a future state advanced distribution planning tool that utilizes machine learning, intelligent distributed edge computing and mixed methods (heuristic, optimization and distributed hierarchical bi-level-game theoretic models) to solve distribution problems and find appropriate/niche solutions.     (1H)

• Member of the SEPA Architecture, Ontology and Transactive Energy working groups, IEEE PES     (1I)