International Conference on Electronic Engineering and Renewable Energy 2018



Dr. Adel Mellit :

Biography:

Dr. Adel Mellit is Professor of Electronics at the Faculty of Sciences and Technology, Jijel University, Algeria. He received his M.S. Degree and Phd in Electronics from the University of Sciences Technologies (USTHB) Algiers in 2002 and 2006 respectively. Research interests of Dr. Adel Mellit focus on the application of the artificial intelligence techniques in photovoltaic systems and micro-grids, control, fault diagnosis, optimization and real time applications. He has authored and co-authored more than 90 papers in international peer reviewed journals (mostly in Elsevier), and papers in conference proceedings (Mostly in IEEE) mainly on photovoltaic systems and six book chapters. He is the Director of the Renewable Energy Laboratory at the Jijel University and is an Associate Member at the ICTP Trieste (Italy). He is actually serving on the Editorial Board of the Renewable Energy and of the Energy Journals (Elsevier Ltd).

Abstract:Application of artificial intelligence techniques in photovoltaic systems: trends, challenges and recommendations

The efficient use of renewable energy is one of the most fundamental challenge to the scientific community. In the Middle East – North African countries, the potential for renewable energy is strongly dominated by solar energy. The photovoltaic generation and concentrating power systems are the most promising renewable energy sources in these countries. Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have also been applied for modeling, identification, optimization, forecasting and control of complex systems. This talk aims to present and discuss the most recent research on the application of AI techniques in photovoltaic systems (Sizing and optimization, output power maximization and forecasting, monitoring and faults diagnosis). Advantages and limits of AI methods in terms of feasibility, complexity, cost-effectiveness and generalization capability and real time integration will be highlighted.

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