Neil Leonard brings a strong set of analytical and mathematical tools to Cadeo. In his role as associate, he excels in problem solving, data management and robust model building. Neil believes scalable and readable code helps bridge the barrier between the client and the technical aspects of a project. With a mindset focused on adaptation, he creates data-driven and resilent solutions. As a result, Neil is highly skilled in modern Python based data science toolsets, including Pandas, BeautifulSoup, and Scikit-Learn.
At Cadeo, Neil’s work has focused on using big data to provide insights on distributed energy resources forecasting and policy development. For example, Neil developed a comprehensive adoption model for Portland General Electric (PGE). This involved crafting a broad framework from scratch and integrating extensive customization options. This model encompassed the analysis of projected peak and energy impacts resulting from DER measure adoption, alongside a thorough examination of the cost effectiveness of DER programs, utilizing Cadeo’s AdopDER model estimates. For Integrated Renewable Energy Council (IREC), Neil used data from the California IOU’s Integrated Capacity Analysis tools to identify hosting capacity trends across their grids. These results could then inform the State’s policies on load additions.
Prior to joining Cadeo, Neil was an analyst with Verdant Associates. There, he assisted in load impact and evaluation analysis, creating reusable code bases for these projects. Neil holds a B.S in Math and Physics from University of Oregon and a M.S. in Mathematical Physics from University of Wisconsin-Madison. In his spare time, Neil enjoys retro videogames and his dogs.