Professor Nabendu Chaki – University of Cacutta, Kolkata, India

Nabendu Chaki is a Professor in the Department Computer Science & Engineering, University of Calcutta, Kolkata, India. He is sharing 7 international patents including 4 US patents. Besides editing more than 30 conference proceedings with Springer, Dr. Chaki has authored 7 text and research books and nearly 200 Scopus Indexed research papers in Journals and International conferences. He has served as a Visiting Professor in different places including Naval Postgraduate School, USA; Ca Foscari University, Italy and AGH University in Poland. He is the founder Chair of ACM Professional Chapter in Kolkata and served in that capacity for 3 years since January 2014. He was active during 2009-2015 towards developing several international standards in Software Engineering and Service Science as a Global (GD) member for ISO-IEC.

Keynote:
Title: A data-driven Approach towards Forecasting Generalized Mid-term Energy Requirement for  Industrial Sector Users of Smart Grid

Abstract:
One of the major improvements that Smart Grid offers over
traditional power grid is a balanced supply demand ratio. As
electricity is hard to store for future usage, it is important to be
aware of the demand in order to generate enough electicity for
uninterrupted power supply. Thus, forecasting plays a vital role in
Smart Grid. However, with various range of rapidly fluctuating
parameters that influence electricity consumption patterns, it is next
to impossible to design a single forecasting model for different types
of users. Typically, electricity usage depends on demographic,
socio-economic and climatic environment of any region. Besides, the
dependencies between influencing parameters and consumption varies
over diffrent sectors, like, residential, commercial and industrial.
In this paper, our main goal is to develop a generalized mid-term
forecasting model for industrial sector, that can accurately predict
quarterly energy usage of a large geographic region with diverse range
of influencing parameters. The proposed model is designed and tested
on real life datasets of industrial users of various states in the
U.S.