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World's Preeminent Machine Learning Experts Bolster C3 Energy Data Science Team [Global Data Point](Global Data Point Via Acquire Media NewsEdge) Smart grid deployments are providing utilities with unprecedented levels of information about the customer and the distribution grid. Hidden in these volumes of grid and meter data are insights that could improve the understanding of customer behavior, detect outages, identify technical and non-technical loss, and more accurately forecast energy demand. Developing these insights requires advanced tools, like machine learning, to help data scientists and analysts discover, analyze, and understand the relationships that exist in the data. Machine learning is the ability for computers to learn without being explicitly programmed. C3 Energy applies machine learning to extract meaning from smart grid data sets much too large for manual processing in order to continuously improve results and adapt to real-world conditions specific utilities. Chief Data Scientist J. Zico Kolter and Senior Data Scientist Henrik Ohlsson lead C3 Energy's machine learning team and are responsible for expanding the team and building on its already impressive expertise. C3 Energy data scientists bring knowledge from extensive research in computer science and engineering fields ranging from large-scale data mining to control theory and statistical analysis at the top universities in the world, including Stanford, University of California, Berkeley, Linkoping University, and Ecole Polytechnique in addition to hands-on experience from positions at NASA, Lawrence Berkeley National Laboratory, and University of Cambridge. "All of this education, experience, and commitment is focused on applying data science and machine learning to enable the future of global energy systems. The amount of data generated by increasing levels of sensors and monitors throughout the grid has reached unprecedented levels. Advanced data science techniques are vital to glean meaningful insights from this data and for utilities to unlock the full social, economic, and environmental value of their smart grid investments," said Kolter. As a regularly published and industry-recognized authority, Kolter applies his expertise in improving the efficiency of power generation using machine learning to the modern energy system. Most recently a member of the faculty at Carnegie Mellon, Kolter earned his doctorate in computer science at Stanford University and conducted extensive postdoctoral research at the MIT Computer Science Artificial Intelligence Laboratory. "It is really spectacular to see that C3 Energy is using some of the most innovative machine learning applications at an unprecedented scale to enable utilities across the globe to modernize the grid and really bring the 'smart' into the smart grid," said S. Shankar Sastry, Dean of Engineering, UC Berkeley. "By applying machine learning to continuously improve smart grid data analytics, utilities are seeing increasingly accurate predictions and control strategies for increasing the efficiency of usage of their smart grids." (c) 2014 GlobalData Provided by SyndiGate Media Inc. (Syndigate.info). |