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Applied Sciences
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Integrating Artificial Intelligence in Renewable Energy Systems
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Integrating Artificial Intelligence in Renewable Energy Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 10578

Special Issue Editors

Prof. Dr. Seongsoo Cho

E-Mail Website
Guest Editor
School of Software, Soongsil University, Seoul 06978, Republic of Korea
Interests: learning/machine learning; image processing; sensor networks; IoT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bhanu Shrestha

E-Mail Website
Guest Editor
Department of Electronic Engineering, Kwangwoon University, Bima Build. #525, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea
Interests: RFIC/MMIC/IPD device and system design; wireless communication; design and fab-rication of device and systems; RF biosensors; ICT convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable energy has emerged as a key player in the ongoing global energy revolution, while artificial intelligence (AI) applications have become a crucial sector that promises to bring about a brighter future for humanity. These two fields have intersected in recent years, resulting in a rapidly expanding area of study. In particular, AI techniques have enhanced the design of renewable energy systems, leading to more sustainable products. These techniques can be applied to various aspects of renewable energy, including improved system design, fault diagnosis, optimal operational conditions, sensitivity analysis, data analysis, decision making, and resource assessment. Additionally, AI can be used to exploit all forms of renewable energy resources such as solar thermal, solar photovoltaic, wind energy, geothermal, and biomass.

The applications of AI and machine learning are not limited to direct power generation but can also be utilized in areas such as air conditioning, building heating and cooling, desalination, and energy storage, making it a powerful tool for any physical, chemical, or biological engineering application.

Prof. Dr. Seongsoo Cho
Prof. Dr. Bhanu Shrestha
Guest Editors

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. Applied Sciences 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

  • hybrid fuzzy systems
  • artificial intelligence and fuzzy systems
  • intelligent systems
  • socio cyber‒-physical systems
  • environmental engineering
  • smart cities
  • healthcare
  • security
  • visualization
  • manufacturing systems
  • logistics
  • telecommunication
  • infrastructure
  • transportation
  • methods and approaches of design
  • development and application of fuzzy systems

Published Papers (10 papers)

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Research

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20 pages, 5675 KiB  
Article
On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting
by Massimo Pacella, Antonio Papa and Gabriele Papadia
Appl. Sci. 2024, 14(8), 3217; https://doi.org/10.3390/app14083217 - 11 Apr 2024
Viewed by 414
Abstract
The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high variability in the weather parameters. [...] Read more.
The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high variability in the weather parameters. Consequently, accurate forecasting has gained increased significance in ensuring a balance between energy supply and demand with maximum efficiency and sustainability. Despite numerous studies on this issue, large sample datasets and measurements of meteorological variables at plant sites are generally required to obtain a higher prediction accuracy. In practical applications, we often encounter the problem of insufficient sample data, which makes it challenging to accurately forecast energy production with limited data. The Holt–Winters exponential smoothing method is a statistical tool that is frequently employed to forecast periodic series, owing to its low demand for training data and high forecasting accuracy. However, this model has limitations, particularly when handling time-series analysis for long-horizon predictions. To overcome this shortcoming, this study proposes an integrated approach that combines the Holt–Winters exponential smoothing method with long short-term memory and Bayesian optimization to handle long-range dependencies. For illustrative purposes, this new method is applied to forecast rooftop photovoltaic production in a real-world case study, where it is assumed that measurements of meteorological variables (such as solar irradiance and temperature) at the plant site are not available. Through our analysis, we found that by utilizing these methods in combination, we can develop more accurate and reliable forecasting models that can inform decision-making and resource management in this field. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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13 pages, 886 KiB  
Article
A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire
by Myoung-Su Choi, Dong-Hun Han, Jun-Woo Choi and Min-Soo Kang
Appl. Sci. 2024, 14(7), 3117; https://doi.org/10.3390/app14073117 - 08 Apr 2024
Viewed by 432
Abstract
Sleep apnea has emerged as a significant health issue in modern society, with self-diagnosis and effective management becoming increasingly important. Among the most renowned methods for self-diagnosis, the STOP-BANG questionnaire is widely recognized as a simple yet effective tool for diagnosing and assessing [...] Read more.
Sleep apnea has emerged as a significant health issue in modern society, with self-diagnosis and effective management becoming increasingly important. Among the most renowned methods for self-diagnosis, the STOP-BANG questionnaire is widely recognized as a simple yet effective tool for diagnosing and assessing the risk of sleep apnea. However, its sensitivity and specificity have limitations, necessitating the need for tools with higher performance. Consequently, this study aimed to enhance the accuracy of sleep apnea diagnoses by integrating machine learning with the STOP-BANG questionnaire. Research through actual cases was conducted based on the data of 262 patients undergoing polysomnography, confirming sleep apnea with a STOP-BANG score of ≥3 and an Apnea–Hypopnea Index (AHI) of ≥5. The accuracy, sensitivity, and specificity were derived by comparing Apnea–Hypopnea Index scores with STOP-BANG scores. When applying machine learning models, four hyperparameter-tuned models were utilized: K-Nearest Neighbor (K-NN), Logistic Regression, Random Forest, and Support Vector Machine (SVM). Among them, the K-NN model with a K value of 11 demonstrated superior performance, achieving a sensitivity of 0.94, specificity of 0.85, and overall accuracy of 0.92. These results highlight the potential of combining traditional STOP-BANG diagnostic tools with machine learning technology, offering new directions for future research in self-diagnosis and the preliminary diagnosis of sleep-related disorders in clinical settings. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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13 pages, 4765 KiB  
Article
A Study on the Measuring Methods of Website Security Risk Rate
by Yong-Joon Lee
Appl. Sci. 2024, 14(1), 42; https://doi.org/10.3390/app14010042 - 20 Dec 2023
Viewed by 562
Abstract
Traditionally, website security risks are measured using static analysis based on patterns and dynamic analysis by accessing websites with user devices. Recently, similarity hash-based website security risk analysis and machine learning-based website security risk analysis methods have been proposed. In this study, we [...] Read more.
Traditionally, website security risks are measured using static analysis based on patterns and dynamic analysis by accessing websites with user devices. Recently, similarity hash-based website security risk analysis and machine learning-based website security risk analysis methods have been proposed. In this study, we propose a technique to measure website risk by collecting public information on the Internet. Publicly available DNS information, IP information, and website reputation information were used to measure security risk. Website reputation information includes global traffic rankings, malware distribution history, and HTTP access status. In this study, we collected public information on a total of 2000 websites, including 1000 legitimate domains and 1000 malicious domains, to assess their security risk. We evaluated 11 categories of public information collected by the Korea Internet & Security Agency, an international domain registrar. Through this study, public information about websites can be collected and used to measure website security risk. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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12 pages, 2763 KiB  
Article
A New Low-Cost Internet of Things-Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation
by Batıkan Erdem Demir
Appl. Sci. 2023, 13(24), 13072; https://doi.org/10.3390/app132413072 - 07 Dec 2023
Cited by 2 | Viewed by 888
Abstract
The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these [...] Read more.
The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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12 pages, 1809 KiB  
Article
A Study on the Performance Evaluation of the Convolutional Neural Network–Transformer Hybrid Model for Positional Analysis
by Sang-Hyun Lee
Appl. Sci. 2023, 13(20), 11258; https://doi.org/10.3390/app132011258 - 13 Oct 2023
Viewed by 696
Abstract
In this study, we identified the different causes of odor problems and their associated discomfort. We also recognized the significance of public health and environmental concerns. To address odor issues, it is vital to conduct precise analysis and comprehend the root causes. We [...] Read more.
In this study, we identified the different causes of odor problems and their associated discomfort. We also recognized the significance of public health and environmental concerns. To address odor issues, it is vital to conduct precise analysis and comprehend the root causes. We suggested a hybrid model of a Convolutional Neural Network (CNN) and Transformer called the CNN–Transformer to tackle this challenge and assessed its effectiveness. We utilized a dataset containing 120,000 samples of odor to compare the performance of CNN+LSTM, CNN, LSTM, and ELM models. The experimental results show that the CNN+LSTM hybrid model has an accuracy of 89.00%, precision of 89.41%, recall of 91.04%, F1-score of 90.22%, and RMSE of 0.28, with a large prediction error. The CNN+Transformer hybrid model had an accuracy of 96.21%, precision and recall of 94.53% and 94.16%, F1-score of 94.35%, and RMSE of 0.27, showing a low prediction error. The CNN model had an accuracy of 87.19%, precision and recall of 89.41% and 91.04%, F1-score of 90.22%, and RMSE of 0.23, showing a low prediction error. The LSTM model had an accuracy of 95.00%, precision and recall of 92.55% and 94.17%, F1-score of 92.33%, and RMSE of 0.03, indicating a very low prediction error. The ELM model performed poorly with an accuracy of 85.50%, precision and recall of 85.26% and 85.19%, respectively, and F1-score and RMSE of 85.19% and 0.31, respectively. This study confirms the suitability of the CNN–Transformer hybrid model for odor analysis and highlights its excellent predictive performance. The employment of this model is expected to be advantageous in addressing odor problems and mitigating associated public health and environmental concerns. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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13 pages, 1249 KiB  
Article
A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
by Myung-Ae Chung, Tae-Hoon Kim, Kyung-A Kim and Min-Soo Kang
Appl. Sci. 2023, 13(18), 10129; https://doi.org/10.3390/app131810129 - 08 Sep 2023
Cited by 1 | Viewed by 804
Abstract
In this study, we applied an optimal deep learning algorithm to detect defects in OLED cells. This study aims to enhance the yield of OLEDs by reducing the number of defective products through defect detection in OLED cells. Defects in OLED cells can [...] Read more.
In this study, we applied an optimal deep learning algorithm to detect defects in OLED cells. This study aims to enhance the yield of OLEDs by reducing the number of defective products through defect detection in OLED cells. Defects in OLED cells can arise owing to various factors, but dark spots are predominantly identified and studied. Therefore, actual dark spot images were required for this study. However, obtaining real data is challenging because of security concerns in the OLED industry. Therefore, a Solver program utilizing the finite element method (FEM) was employed to generate 2000 virtual dark spot images. The generated images were categorized into two groups: initial images of dark spots and images obtained after 10,000 h. The pixel values of these dark spot images were adjusted for efficient recognition and analysis. Furthermore, CNN, ResNet-50, and VGG-16 were implemented to apply the optimal deep learning algorithms. The results showed that the VGG-16 algorithm performed the best. A defect detection model was created based on the performance metrics of the deep learning algorithms. The model was trained using 1300 dark spot images and validated using 600 dark spot images. The validation results indicated an accuracy of 0.988 and a loss value of 0.026. A defect detection model that utilizes the VGG-16 algorithm was considered suitable for distinguishing dark spot images. To test the defect detection model, 100 images of dark spots were used. The experimental results indicated an accuracy of 89%. The images were classified as acceptable or defective based on the threshold values. By applying the VGG-16 deep learning algorithm to the defect detection model, we can enhance the yield of OLED products, reduce production costs, and contribute significantly to the advancement of OLED display manufacturing technology. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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11 pages, 2653 KiB  
Article
Performance Evaluation of Machine Learning and Deep Learning-Based Models for Predicting Remaining Capacity of Lithium-Ion Batteries
by Sang-Hyun Lee
Appl. Sci. 2023, 13(16), 9127; https://doi.org/10.3390/app13169127 - 10 Aug 2023
Cited by 1 | Viewed by 916
Abstract
Lithium-ion batteries are widely used in electric vehicles, smartphones, and energy storage devices due to their high power and light weight. The goal of this study is to predict the remaining capacity of a lithium-ion battery and evaluate its performance through three machine [...] Read more.
Lithium-ion batteries are widely used in electric vehicles, smartphones, and energy storage devices due to their high power and light weight. The goal of this study is to predict the remaining capacity of a lithium-ion battery and evaluate its performance through three machine learning models: linear regression, decision tree, and random forest, and two deep learning models: neural network and ensemble model. Mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R-squared), and root mean squared error (RMSE) were used to measure prediction accuracy. For the evaluation of the artificial intelligence model, the dataset was downloaded and integrated with measurement data of the CS2 lithium-ion battery provided by the University of Maryland College of Engineering. As a result of the study, the RMSE of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. According to the measured values, the ensemble model showed the best predictive performance, followed by the neural network model. Decision tree and random forest models also showed very good performance, and the linear regression model showed relatively poor predictive performance compared to the other models. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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10 pages, 1507 KiB  
Article
A Study on OLED Cell Simulation and Detection Phases Based on the A2G Algorithm for Artificial Intelligence Application
by Dong-Hun Han, Yeong-Hoon Jeong and Min-Soo Kang
Appl. Sci. 2023, 13(14), 8016; https://doi.org/10.3390/app13148016 - 09 Jul 2023
Cited by 2 | Viewed by 1469
Abstract
In this study, we demonstrate the viability of applying artificial intelligence (AI) techniques to conduct inspections at the OLED cell level using simulated data. The implementation of AI technologies necessitates training data, which we addressed by generating an OLED dataset via our proprietary [...] Read more.
In this study, we demonstrate the viability of applying artificial intelligence (AI) techniques to conduct inspections at the OLED cell level using simulated data. The implementation of AI technologies necessitates training data, which we addressed by generating an OLED dataset via our proprietary A2G algorithm, integrating the finite element method among concerns over data security. Our A2G algorithm is designed to produce time-dependent datasets and establish threshold conditions for the expansion of dark spots based on OLED parameters and predicted lifespan. We explored the potential integration of AI in the inspection phase, performing cell-based evaluations using three distinct convolutional neural network models. The test results yielded a promising 95% recognition rate when classifying OLED data into pass and fail categories, demonstrating the practical effectiveness of this approach. Through this research, we not only confirmed the feasibility of using simulated OLED data in place of actual data but also highlighted the potential for the automation of manual inspection processes. Furthermore, by introducing OLED defect detection models at the cell level, as opposed to the traditional panel level during inspections, we anticipate higher classification rates and improved yield. This forward-thinking approach underscores significant advancement in OLED inspection processes, indicating a potential shift in industry standards. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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25 pages, 5358 KiB  
Article
Load Forecasting with Machine Learning and Deep Learning Methods
by Moisés Cordeiro-Costas, Daniel Villanueva, Pablo Eguía-Oller, Miguel Martínez-Comesaña and Sérgio Ramos
Appl. Sci. 2023, 13(13), 7933; https://doi.org/10.3390/app13137933 - 06 Jul 2023
Cited by 9 | Viewed by 3018
Abstract
Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field [...] Read more.
Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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Review

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29 pages, 6075 KiB  
Review
A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm
by Guy M. Toche Tchio, Joseph Kenfack, Djima Kassegne, Francis-Daniel Menga and Sanoussi S. Ouro-Djobo
Appl. Sci. 2024, 14(5), 2072; https://doi.org/10.3390/app14052072 - 01 Mar 2024
Viewed by 662
Abstract
Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. [...] Read more.
Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. In this article, we propose a supervised learning algorithm that can detect and classify PV system defects. We delve into the world of supervised learning-based machine learning and its application in detecting and classifying defects in photovoltaic (PV) systems. We explore the various types of faults that can occur in a PV system and provide a concise overview of the most commonly used machine learning and supervised learning techniques in diagnosing such systems. Additionally, we introduce a novel classifier known as Extra Trees or Extremely Randomized Trees as a speedy diagnostic approach for PV systems. Although this algorithm has not yet been explored in the realm of fault detection and classification for photovoltaic installations, it is highly recommended due to its remarkable precision, minimal variance, and efficient processing. The purpose of this article is to assist technicians, engineers, and researchers in identifying typical faults that are responsible for PV system failures, as well as creating effective control and supervision techniques that can minimize breakdowns and ensure the longevity of installed systems. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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