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Professor, Department of Mechanical and Industrial Engineering
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Dr. Andrew Kusiak is a Professor in the Department of Mechanical and Industrial Engineering at The University of Iowa, Iowa City and Director of Intelligent Systems Laboratory. His current research interests include applications of computational intelligence and big data in renewable energy, automation, manufacturing, product development, sustainability, and healthcare. He is the author or coauthor of numerous books and hundreds of technical papers published in journals sponsored by professional societies, such as the Association for the Advancement of Artificial Intelligence, the American Society of Mechanical Engineers, Institute of Industrial Engineers, Institute of Electrical and Electronics Engineers, and other societies. He speaks frequently at international meetings, conducts professional seminars, and consults for industrial corporations. Dr. Kusiak has served in elected professional society positions as well as various editorial boards of over fifty journals, including five different IEEE Transactions.
Big Data in Energy: From Generation to Consumption Perspective
Big data is beginning to shape the development of solutions in energy generation, distribution, and consumption. The newly developed solutions lead to systems that are smarter, better connected, more efficient, and sustainable. Energy generation, distribution, and consumption are significant components of the sustainability equation. Data science offers a unifying approach for tackling problems across different phases of electricity life cycle. It supports progress towards sustainability.
Energy engineers need to embrace applications where data offers value. The electricity life cycle is a highly multidisciplinary process awaiting new solutions. It is widely recognized that design of innovative solutions considers data streams originating at users, experts, energy flow, and the cyberspace. Data science offers tools for fusing the diverse steams of data that would not be possible with traditional modeling or simulation approaches.
Sustainability considerations contribute another data science challenge. Data science developments in energy need to include energy generation using clean technologies and management of its consumption. The latter includes approaches to maximize energy conservation. The energy saved does not need to be generated which offers a valuable economic option.