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Sustainability
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Embedded System Applications in Solar Photovoltaics
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Embedded System Applications in Solar Photovoltaics

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 3072

Special Issue Editor

Prof. Dr. Adel Mellit

E-Mail Website
Guest Editor
Faculty of Science and Technology, University of Jijel, Jijel, Algeria
Interests: application of artificial intelligence techniques in photovoltaic systems; energy management systems; implementation of intelligent methods in MC; dSPACE and FPGA; solar energy applications; micro-grid applications (electrical vehicle charging station); smart monitoring and remote sensing systems based IoT technique

Special Issue Information

Dear Colleagues,

Embedded systems have presented their capability to address various engineering problems, such as electric vehicles, charging stations, the health sector, factory robot, and medical devices. These forms of embedded systems have been developed and designed to perform specific tasks. To date, few studies on the area of solar energy, particularly photovoltaic systems, exist in the literature. The majority of the available research concerning embedded systems in the solar energy field have attempted to use and integrate recent techniques (e.g., machine learning, deep learning, and the Internet of Things) and technologies (e.g., smart sensors, SoC, and reconfigurable devices) to solve certain problems, such as monitoring, fault diagnosis, optimization, and control. The embedded systems play a mission-critical role in solar energy applications and contribute to advance and develop the research conducted this sector. This Special Issue aims to focus on the application of embedded systems in photovoltaic installations, including stand-alone, grid-connected, and hybrid systems. The real-time integration of such methods into reconfigurable circuits (e.g., CPLD and FPGA), microcontrollers (STM32 and Arduino), microprocessors (e.g., Raspberry Pi), and the Android App are invited for submission. Researchers are encouraged to submit high-quality papers related to the following topics:

- Artificial intelligence and Internet of Things for photovoltaics ;

- Android and IOS Apps for photovoltaics;

- Embedded machine learning and deep learning for:

  • Monitoring systems;
  • Fault-detection and isolation techniques;
  • Fault-diagnosis methods (such as areal inspection, electrical, and images);
  • Energy-management methods;
  • Optimization and control methods.

Prof. Dr. Adel Mellit
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • photovoltaics
  • machine learning
  • deep learning
  • IoT
  • embedded ML
  • edge device
  • monitoring
  • fault diagnosis
  • control and optimization

Published Papers (2 papers)

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Research

20 pages, 3562 KiB  
Article
Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy
by Yasemin Ayaz Atalan and Abdulkadir Atalan
Sustainability 2023, 15(18), 13782; https://doi.org/10.3390/su151813782 - 15 Sep 2023
Cited by 2 | Viewed by 919
Abstract
The importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and [...] Read more.
The importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for the amount of solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boost or AdaBoost (AB) algorithms from ML models. Data belonging to eight independent variables consisting of environmental and geographical factors were used. This study consists of approximately two years of data on the amount of solar energy production for 636 days. The study consisted of three stages: First, descriptive statistics and analysis of variance tests of the dependent and independent variables were performed. In the second stage of the method, estimation data for the amount of solar energy production, representing the dependent variable, were obtained from AB, RF, GB, and LR algorithms and ML models. The AB algorithm performed best among the ML models, with the lowest RMSE, MSE, and MAE values and the highest R2 value for the forecast data. For the estimation phase of the AB algorithm, the RMSE, MSE, MAE, and R2 values were calculated as 0.328, 0.107, 0.134, and 0.909, respectively. The RF algorithm performed worst with performance scores for the prediction data. The RMSE, MSE, MAE, and R2 values of the RF algorithm were calculated as 0.685, 0.469, 0.503, and 0.623, respectively. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. At the end of all phases, this study aimed to validate the results obtained by integrating the two techniques. Therefore, this study offers a critical perspective to demonstrate a two-way verification approach to whether a system’s forecast data are under control for the future. Full article
(This article belongs to the Special Issue Embedded System Applications in Solar Photovoltaics)
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20 pages, 10181 KiB  
Article
Embedded Hybrid Model (CNN–ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images
by Mohamed Benghanem, Adel Mellit and Chourouk Moussaoui
Sustainability 2023, 15(10), 7811; https://doi.org/10.3390/su15107811 - 10 May 2023
Cited by 4 | Viewed by 1447
Abstract
In this paper, a novel hybrid model for the fault diagnosis of photovoltaic (PV) modules was developed. The model combines a convolutional neural network (CNN) with a machine learning (ML) algorithm. A total of seven defects were considered in this study: sand accumulated [...] Read more.
In this paper, a novel hybrid model for the fault diagnosis of photovoltaic (PV) modules was developed. The model combines a convolutional neural network (CNN) with a machine learning (ML) algorithm. A total of seven defects were considered in this study: sand accumulated on PV modules, covered PV modules, cracked PV modules, degradation, dirty PV modules, short-circuited PV modules, and overheated bypass diodes. First, the hybrid CNN–ML has been developed to classify the seven common defects that occur in PV modules. Second, the developed model has been then optimized. Third, the optimized model has been implemented into a microprocessor (Raspberry Pi 4) for real-time application. Finally, a friendly graphical user interface (GUI) has been designed to help users analyze their PV modules. The proposed hybrid model was extensively evaluated by a comprehensive database collected from three regions with different climatic conditions (Mediterranean, arid, and semi-arid climates). Experimental tests showed the feasibility of such an embedded solution in the diagnosis of PV modules. A comparative study with the state-of-the-art models and our model has been also presented in this paper. Full article
(This article belongs to the Special Issue Embedded System Applications in Solar Photovoltaics)
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