Contacts
Data Engineering promotes an engineering approach to analyze big “imperfect” data by creating and using new algorithms such as Unsupervised Deep Learning, appropriate electronic hardware platforms and mechanical/mechatronic/chemical-based approaches to interrogate and acquire missing data or information. My research engages a comprehensive data engineering approach from sensors for data collection to data analytics and optimization. This research has significant applications in Energy, Health, Agriculture and Business sectors and IoT applications.
[1] K. Abdulla, K. Steer, A. Wirth, and S.K. Halgamuge, “Improving the On-line Control of Energy Storage via Forecast Error Metric Customization”, Journal of Energy Storage, accepted in Sep 2016, Elsevier.
[2] K. Abdulla, J. d. Hoog, V. Muenzel, F. Suits, K. Steer, A. Wirth, and S. K. Halgamuge, “Optimal Operation of Energy Storage Systems Considering Forecasts and Battery Degradation", IEEE Transactions on Smart Grids, accepted in September 2016.
[3] A. Habibi, T. Scherer and S.K. Halgamuge, “Energy, Environmental and Economical Saving Potential of Data Centers with Various Economizers across Australia”, Applied Energy, accepted in 2016, Elsevier.