D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 56 Citations 10,710 162 World Ranking 955 National Ranking 2

Overview

What is he best known for?

The fields of study he is best known for:

  • Electrical engineering
  • Artificial intelligence
  • Machine learning

His primary scientific interests are in Photovoltaic system, Artificial neural network, Fault detection and isolation, Fault and Sizing. His Photovoltaic system research is multidisciplinary, incorporating elements of Maximum power point tracking, Fuzzy logic, Artificial intelligence, Field-programmable gate array and Multilayer perceptron. His Field-programmable gate array research includes elements of Photovoltaics, Membership function and Voltage.

His studies in Artificial neural network integrate themes in fields like Solar irradiance, Markov model, Correlation coefficient and Sunshine duration. His Fault detection and isolation study incorporates themes from Diagnosis methods, Converters, Electronic engineering, Pv plant and Reliability. Adel Mellit combines subjects such as Reliability engineering and Arc-fault circuit interrupter with his study of Fault.

His most cited work include:

  • A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy (545 citations)
  • Artificial intelligence techniques for photovoltaic applications: A review (542 citations)
  • Artificial intelligence techniques for sizing photovoltaic systems: A review (278 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Photovoltaic system, Artificial neural network, Control theory, Electronic engineering and Maximum power point tracking. His work on Maximum power principle as part of general Photovoltaic system research is often related to Sizing, thus linking different fields of science. His Artificial neural network study also includes

  • Correlation coefficient, which have a strong connection to Meteorology and Convolutional neural network,
  • Solar irradiance which intersects with area such as Irradiance and Photovoltaics.

His studies deal with areas such as Induction motor and Power control as well as Control theory. His research integrates issues of Field-programmable gate array and MATLAB in his study of Electronic engineering. In his work, Control theory, Control engineering and Hybrid system is strongly intertwined with Fuzzy logic, which is a subfield of Maximum power point tracking.

He most often published in these fields:

  • Photovoltaic system (82.22%)
  • Artificial neural network (31.67%)
  • Control theory (23.89%)

What were the highlights of his more recent work (between 2019-2021)?

  • Photovoltaic system (82.22%)
  • Artificial intelligence (17.78%)
  • Deep learning (4.44%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Photovoltaic system, Artificial intelligence, Deep learning, Control theory and Variable. His Photovoltaic system research includes themes of Correlation coefficient, Solar irradiance, Fault detection and isolation, Maximum power point tracking and Convolutional neural network. His studies examine the connections between Correlation coefficient and genetics, as well as such issues in Artificial neural network, with regards to Energy management.

Adel Mellit interconnects Fault and Machine learning in the investigation of issues within Artificial intelligence. The Deep learning study combines topics in areas such as Control engineering, Applications of artificial intelligence, Electronic engineering and Identification. In his research, Microgrid is intimately related to Virtual impedance, which falls under the overarching field of Control theory.

Between 2019 and 2021, his most popular works were:

  • Advanced Methods for Photovoltaic Output Power Forecasting: A Review (26 citations)
  • A Low-Cost Monitoring and Fault Detection System for Stand-Alone Photovoltaic Systems Using IoT Technique (6 citations)
  • Experimental Evidence of PID Effect on CIGS Photovoltaic Modules (3 citations)

In his most recent research, the most cited papers focused on:

  • Electrical engineering
  • Artificial intelligence
  • Machine learning

Adel Mellit mainly investigates Photovoltaic system, Artificial intelligence, Deep learning, Internet of Things and Electrical engineering. The concepts of his Photovoltaic system study are interwoven with issues in Optical modeling and Chemical engineering. His work deals with themes such as Machine learning, Solar forecasting, Series and Global solar radiation, which intersect with Artificial intelligence.

Adel Mellit has included themes like Fault, Applications of artificial intelligence, Electronic engineering and Control engineering in his Deep learning study.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

Adel Mellit;Alessandro Massi Pavan.
Solar Energy (2010)

794 Citations

Artificial intelligence techniques for photovoltaic applications: A review

Adel Mellit;Soteris A. Kalogirou.
Progress in Energy and Combustion Science (2008)

670 Citations

Artificial intelligence techniques for sizing photovoltaic systems: A review

A. Mellit;S. A. Kalogirou;L. Hontoria;Sulaiman Shaari.
Renewable & Sustainable Energy Reviews (2009)

382 Citations

A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

W. Chine;A. Mellit;A. Mellit;V. Lughi;A. Malek.
Renewable Energy (2016)

305 Citations

An adaptive wavelet-network model for forecasting daily total solar-radiation

Adel Mellit;Mohamed S. Benghanem;Soteris A. Kalogirou.
Applied Energy (2006)

285 Citations

ANN-based modelling and estimation of daily global solar radiation data: A case study

M. Benghanem;A. Mellit;S.N. Alamri.
Energy Conversion and Management (2009)

267 Citations

Maximum power point tracking using a GA optimized fuzzy logic controller and its FPGA implementation

A. Messai;A. Mellit;A. Guessoum;S.A. Kalogirou.
Solar Energy (2011)

252 Citations

Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure

Adel Mellit;Mohamed S. Benghanem;Soteris A. Kalogirou.
Renewable Energy (2007)

238 Citations

A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant

M. Bouzerdoum;A. Mellit;A. Massi Pavan.
Solar Energy (2013)

219 Citations

A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach

A. Mellit;M. Benghanem;A. Hadj Arab;A. Guessoum.
Solar Energy (2005)

197 Citations

Editorial Boards

Energy
(Impact Factor: 8.857)

Renewable Energy
(Impact Factor: 8.634)

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