Saptarsi Goswami

Dr. Saptarsi Goswami

Assistant Professor, Bangabasi Morning College, Calcutta University

Dr. Saptarsi Goswami comes with 20 + years of experience with 10 + years in both Industry and academia. He is a B.Tech from NIT, Jaipur. His industry experience has been mostly in the area of data engineering in Tata Infotech, PwC, and Cognizant. He followed up his love for Data with an M,Tech and PhD from Calcutta University in the domain of data mining and machine learning. He is currently working as an Assistant Professor and Head of Comp Sc in a Govt Aided College affiliated with the University of Calcutta. He is playing the role of honorary advisor to companies like AcheiveX Solution Pvt Ltd, Dirac Business Solution Pvt Ltd, etc. For the last 2.5 years, he is also working as Digital Transformation Lead, in the Technology Innovation Cell of the Higher Education Department., Govt of WB. He is also the coordinator of the International Center of Excellence DS, AI & FT. His YouTube lectures are part of National Digital Library of India and was adjudged as Resource Person of the Month in the year 2020. He was a foundational member in setting up the Data Science Lab at Calcutta University and has 1500 + citations of his published research as of today. He is passionate about building the community and has played the role of ODSC Chapter Lead as well as Azure Community Development Lead for the City of Joy.

Session

Deep Learning for Sustainable Energy: A Journey


Overview

In the current era marked by a surging demand for energy, the emphasis on sustainable energy sources is more crucial than ever. Among these, Solar Energy stands out as a pivotal contributor to renewable energy. However, the effective utilization of solar energy necessitates precise forecasting, a critical aspect from both grid planning and regulatory standpoints. The challenges in India are particularly noteworthy due to the diverse climate regions with their unique intricacies. It has been observed that conventional forecasting models often exhibit limitations in performance. In this presentation, we aim to shed light on our research endeavors, featured in prominent academic journals and conducted in collaboration with the National Institute of Wind Energy (NIWE) under the Ministry of New and Renewable Energy (MNRE). Our discussion will delve into the challenges prevailing in the current state-of-the-art methodologies and outline our systematic approach to address these challenges step by step.


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