Theoretical Study of Quantum Well GaAsP(N)/GaP Structures for Solar Cells

Chenini, L.; Aissat, A.

doi:10.1007/978-3-030-43473-1_4

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…To make a simulation on strained GaAsxP1-x and tensile train GaNyAsxP1-x-y in the quantum zones that are well active to get it inserted in the solar cells. Chenini et al [29] used the band anticrossing model to explain the dependencies of lower E-and lower E + sub-band energies of the nitrogen concentration. The donor-acceptor approach has been used in this study for preparing low bandgap polymers concluding that the introduction of arsenic into the host material leads to the reduction of the bandgap energy.…”

Section: Review Based Analysismentioning

confidence: 99%

“…Recent research work done in electricity forecasting and the PV generation domain has been reviewed and analysed to select the best model and find novel approaches [28][29][30]. One of the most relevant novelties of the paper is an analysis of the state of the art performed in three different phases, that has not been found in the current literature:…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Techniques for the Photovoltaic System: A Systematic Review and Analysis for Evaluation and Benchmarking

Kumar,

Dubey,

Segovia Ramírez

et al. 2024

Arch Computat Methods Eng

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Novel algorithms and techniques are being developed for design, forecasting and maintenance in photovoltaic due to high computational costs and volume of data. Machine Learning, artificial intelligence techniques and algorithms provide automated, intelligent and history-based solutions for complex scenarios. This paper aims to identify through a systematic review and analysis the role of artificial intelligence algorithms in photovoltaic systems analysis and control. The main novelty of this work is the exploration of methodological insights in three different ways. The first approach is to investigate the applicability of artificial intelligence techniques in photovoltaic systems. The second approach is the computational study and analysis of data operations, failure predictors, maintenance assessment, safety response, photovoltaic installation issues, intelligent monitoring etc. All these factors are discussed along with the results after applying the artificial intelligence techniques on photovoltaic systems, exploring the challenges and limitations considering a wide variety of latest related manuscripts.

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Section: Review Based Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%