Data Analytics & Optimization
Data analytics can help us solve today’s complex energy challenges. The influence of data science in the clean energy sector continues to expand with major algorithmic developments in machine learning, relentless growth in computing power and advancements in sensor technology and Internet of Things (IoT) capabilities.
Data comes in many different shapes, sizes and volumes. Manufacturing sites in particular, are in possession of mountains of industrial data that are often underutilized and ripe for value extraction. These incredible volumes of data, when combined with expert domain knowledge and advanced analytics capabilities, are poised to elucidate valuable insights never realized before in the clean energy sector.
FEATURE PROJECTS
- Energy Management in Buildings (Research Summary)
- Advanced Battery Management System for Lithium-Ion Batteries (Research Summary)
- Deep Learning-based Model Predictive Control (Research Summary)
Challenges and Opportunities
Data-driven policy making: Using data science to inform better policy design, planning and implementation under uncertainty
Advanced analytics for energy management: Harnessing the power of machine learning and optimization to improve energy efficiency and sustainability
Asset optimization: Monitoring equipment health in solar plants and wind farms and optimizing assets for peak performance using data analytics
Theme members
- Ahmad Al-Dabbagh
- Shahria Alam
- Dan Bizzotto
- Yankai Cao
- Christine Chen
- Keng C. Chou
- Roland Clift
- Kelly Clifton
- Edmond Cretu
- Peter Englezos
Theme leader
