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Imran Khan

PhD Graduate

Email: ikr_ece@yahoo.com

ImranKhanGraduate (160)Imran Khan completed his PhD degree in Energy Management Programme at the University of Otago, New Zealand in 2019. He was in receipt of a University of Otago Doctoral Scholarship. He completed his M.Sc. Engineering degree (combined) from University of Ottawa, Canada and Khulna University of Engineering and Technology, Bangladesh. He graduated with a Bachelor of Science in ECE from Khulna University of Engineering and Technology, Bangladesh. Before starting his PhD at University of Otago, he was involved in different research projects and teaching in Bangladesh.

Research 

Imran's research interests include multi-disciplinary approaches toward electrical energy management such as peak demand management. His research also includes GHG emission assessment from electricity generation, electricity generation sustainability assessment, residential peak demand characterization, renewable energy application to demand-side management, and prosumerism.

Thesis

A Temporal Approach to Characterizing Electrical Peak Demand: Assessment of GHG Emissions at the Supply Side and Identification of Dominant Household Factors at the Demand Side. Available at: http://hdl.handle.net/10523/9185

Publications

Khan I. (2019). Drivers, enablers, and barriers to prosumerism in Bangladesh: A sustainable solution to energy poverty? Energy Research & Social Science, Elsevier. Vol.55, pp.82-92. Available at: https://doi.org/10.1016/j.erss.2019.04.019

 Khan I., Jack M.W., Stephenson J. (2019). Identifying residential daily electricity-use profiles through time-segmented regression analysis, Energy & Buildings, Elsevier. Vol 194, 232-246. Available at: https://doi.org/10.1016/j.enbuild.2019.04.026

Khan, I. (2019). Energy saving behaviour as a demand side management strategy in the developing world: the case of Bangladesh, International Journal of Energy and Environmental Engineering, Springer. Pp 1-18. Available at:  https://doi.org/10.1007/s40095-019-0302-3

Khan I. (2019). Power generation expansion plan and sustainability in a developing country: A multi-criteria decision analysis. Journal of Cleaner Production. Elsevier. Vol.220, pp.707- 720.  Available at: https://doi.org/10.1016/j.jclepro.2019.02.161

Khan, I. (2019). Household factors and electrical peak demand : a review for further assessment. Advances in Building Energy Research, Taylor & Francis.  pp. 1–33. Available at:  https://doi.org/10.1080/17512549.2019.1575770

Khan, I. (2018). Greenhouse gas emission accounting approaches in electricity generation systems: A review. Journal of Atmospheric Environment, Elsevier. Vol 200, pp 131–141. Available at: https://doi.org/10.1016/j.atmosenv.2018.12.005

Khan, I. (2018). Temporal carbon intensity analysis: renewable versus fossil fuel dominated electricity systems. Energy Sources, Part A: Recovery, Utilization, and
Environmental Effects
. Taylor & Francis. Vol 41, pp 309-323.  Available at: https://doi.org/10.1080/15567036.2018.1516013

Khan, I. (2018). Importance of GHG emissions assessment in the electricity grid expansion towards a low-carbon future: A time-varying carbon intensity approach. Journal of Cleaner Production, Elsevier. Vol 196, pp 1587–1599. Available at: https://doi.org/10.1016/j.jclepro.2018.06.162

Khan, I., Jack, M.W., Stephenson, J. (2018). Analysis of greenhouse gas emissions in electricity systems using time-varying carbon intensity. Journal of Cleaner Production, Elsevier. Vol 184, 1091–1101. Available at: https://doi.org/10.1016/j.jclepro.2018.02.309

Khan I. (2018), Time-segmented Regression Analysis : An Approach in Designing more Effective DSM Scheme, 6th IEEE Conference on Technologies for Sustainability, IEEE, Long Beach, CA, USA, pp. 1–2.
Available at: https://doi.org/10.1109/SusTech.2018.8671364

I. Khan, M.W. Jack, J. Stephenson (2017). Use of Time-varying Carbon Intensity Estimation to Evaluate GHG Emission Reduction Opportunities in Electricity Sector, 5th IEEE Conference on Technologies for Sustainability., IEEE, Phoenix, AZ, USA. pp. 1–2. https://doi.org/10.1109/SusTech.2017.8333479