Pub Date : 2025-07-22DOI: 10.23919/SAIEE.2025.11090063
Boriane Y. Tchaleu;Alain R. Ndjiongue;Collins A. Leke
The deep learning ability to recognize patterns in data has recently become popular within education. Created in 2014, generative adversarial networks (GANs) are innovative classes of deep learning generative models based on game theory and consist of two players. GANs generate data from scratch using two neural networks: the generator and the discriminator. Since their creation, GANs have been utilized in many applications and have advantages and disadvantages. In light of such a long journey, evaluating the technology is essential as it provides readers with the way forward. To this end, this paper reviews GANs and explores some fundamental challenges that develop during evaluation and training. We also discuss GANs' challenges and elaborate subsequent solutions. Through a single context, we explain the reasoning behind the GAN technology and examine its direction and motivation. We discuss different variants of GANs and real-world application examples, including performance evaluation metrics across various sectors. We consider results obtained recently and highlight ideas for further investigation. This detailed retrospect will give the reader a better understanding of the possible uses of GANs. It will also show how they can help address current issues in a variety of disciplines. Before that, the paper reviews GANs' architectures and network approaches and elaborates on challenges and solutions. The reader is then guided through the literature on the various applications of GANs and the importance of the research interest associated with GANs. As a final step, we suggest the way forward and conclude the review.
{"title":"Generative adversarial networks: A comprehensive review and the way forward","authors":"Boriane Y. Tchaleu;Alain R. Ndjiongue;Collins A. Leke","doi":"10.23919/SAIEE.2025.11090063","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.11090063","url":null,"abstract":"The deep learning ability to recognize patterns in data has recently become popular within education. Created in 2014, generative adversarial networks (GANs) are innovative classes of deep learning generative models based on game theory and consist of two players. GANs generate data from scratch using two neural networks: the generator and the discriminator. Since their creation, GANs have been utilized in many applications and have advantages and disadvantages. In light of such a long journey, evaluating the technology is essential as it provides readers with the way forward. To this end, this paper reviews GANs and explores some fundamental challenges that develop during evaluation and training. We also discuss GANs' challenges and elaborate subsequent solutions. Through a single context, we explain the reasoning behind the GAN technology and examine its direction and motivation. We discuss different variants of GANs and real-world application examples, including performance evaluation metrics across various sectors. We consider results obtained recently and highlight ideas for further investigation. This detailed retrospect will give the reader a better understanding of the possible uses of GANs. It will also show how they can help address current issues in a variety of disciplines. Before that, the paper reviews GANs' architectures and network approaches and elaborates on challenges and solutions. The reader is then guided through the literature on the various applications of GANs and the importance of the research interest associated with GANs. As a final step, we suggest the way forward and conclude the review.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 3","pages":"101-124"},"PeriodicalIF":1.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11090063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.23919/SAIEE.2025.10852573
A. Ferrer-Rojas;B. T. J. Maharaj
The rapid and expansive integration of Internet of Things (IoT) environments across various industrial sectors has led to an unprecedented surge in data generation and management. This exponential growth in data underscores the critical necessity for robust data security methodologies that can effectively safeguard the confidentiality and integrity of information without imposing undue computational burdens. In response to this challenge, numerous studies have sought to leverage Attribute-Based Encryption (ABE) as a means to enable fine-grained access control. Among the ABE variants, Ciphertext Policy ABE (CP-ABE) and bilinear pairings have emerged as popular choices to construct security schemes that strike a balance between robust protection and computational efficiency. Despite the advancements achieved through CP-ABE and bilinear pairings, a prevalent concern arises in the utilization of Linear Secret Sharing Scheme (LSSS) access policies. LSSS policies, while providing a flexible and expressive way to define access controls, can significantly impact the execution time of encryption methods. This study recognizes the importance of addressing this challenge and explores the potential of employing a Key Policy Attribute-Based Encryption (KP-ABE) approach. The primary objective is to mitigate the computational overhead associated with encryption methods, thereby enhancing the efficiency of data security measures within IoT environments. Furthermore, this research delves into the incorporation of Elliptic Curve Cryptography (ECC) to generate cryptographic keys. ECC, known for its strong security properties and computational efficiency, is considered a promising approach to bolster data security while concurrently minimizing computational overhead. By integrating KP-ABE with ECC, this study aims to offer a comprehensive solution that ensures robust security measures within the intricate landscape of IoT environments. Through detailed analysis and empirical investigation, the research endeavors to contribute valuable insights to the ongoing discourse on securing IoT data in a manner that aligns with the dual imperatives of security and computational efficiency.
{"title":"Multiauthority KP-ABE access model with elliptic curve cryptography","authors":"A. Ferrer-Rojas;B. T. J. Maharaj","doi":"10.23919/SAIEE.2025.10852573","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10852573","url":null,"abstract":"The rapid and expansive integration of Internet of Things (IoT) environments across various industrial sectors has led to an unprecedented surge in data generation and management. This exponential growth in data underscores the critical necessity for robust data security methodologies that can effectively safeguard the confidentiality and integrity of information without imposing undue computational burdens. In response to this challenge, numerous studies have sought to leverage Attribute-Based Encryption (ABE) as a means to enable fine-grained access control. Among the ABE variants, Ciphertext Policy ABE (CP-ABE) and bilinear pairings have emerged as popular choices to construct security schemes that strike a balance between robust protection and computational efficiency. Despite the advancements achieved through CP-ABE and bilinear pairings, a prevalent concern arises in the utilization of Linear Secret Sharing Scheme (LSSS) access policies. LSSS policies, while providing a flexible and expressive way to define access controls, can significantly impact the execution time of encryption methods. This study recognizes the importance of addressing this challenge and explores the potential of employing a Key Policy Attribute-Based Encryption (KP-ABE) approach. The primary objective is to mitigate the computational overhead associated with encryption methods, thereby enhancing the efficiency of data security measures within IoT environments. Furthermore, this research delves into the incorporation of Elliptic Curve Cryptography (ECC) to generate cryptographic keys. ECC, known for its strong security properties and computational efficiency, is considered a promising approach to bolster data security while concurrently minimizing computational overhead. By integrating KP-ABE with ECC, this study aims to offer a comprehensive solution that ensures robust security measures within the intricate landscape of IoT environments. Through detailed analysis and empirical investigation, the research endeavors to contribute valuable insights to the ongoing discourse on securing IoT data in a manner that aligns with the dual imperatives of security and computational efficiency.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 2","pages":"59-67"},"PeriodicalIF":1.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.23919/SAIEE.2025.10852569
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2025.10852569","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10852569","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 2","pages":"44-44"},"PeriodicalIF":1.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.23919/SAIEE.2025.10852565
{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2025.10852565","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10852565","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 2","pages":"79-79"},"PeriodicalIF":1.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.23919/SAIEE.2025.10852572
T. Fu;R. Sun;C. Li;L. Wang
Cultivating students' software design capabilities through effective training has always been a challenge in software education. This paper is aimed at addressing this issue by adopting a multi-scenario case study approach to examine the independent design processes of 23 undergraduate students on an online teaching system. The selected case scenario models include transaction flow diagrams (TFDs), activity diagrams, sequence diagrams, and entity-relationship (ER) diagrams. By analyzing students' behavioral performances and design outcomes, a series of common phenomena are identified. These phenomena encompass common errors, such as overlooking key steps, struggling to distinguish similar data objects, and omitting critical entities or attributes. Common behaviors include offering various solutions, facing challenges in achieving specific design goals due to a lack of prior experience, and experiencing difficulties meeting requirements using prescribed syntax. Common approaches to assist students include providing reference software, adopting teamwork for idea generation, and allowing iterative modifications to improve outcomes. Based on these common phenomena exhibited by students, several recommendations are provided for software educators to enhance the development of students' software design capabilities, which mainly include considering students' prior experience in assignments, providing design references for unfamiliar software, encouraging peer discussions and multiple iterations, and guiding students towards continuous improvement rather than disregarding unconventional outcomes. The common phenomena identified in this paper seamlessly integrate with software design education, reflecting its distinct characteristics. Those common phenomena will help researchers understand student needs and challenges. Additionally, the research design, which analyzes student behaviors based on their software design outcomes, provides a fresh perspective in the field. Furthermore, the conclusions drawn in this paper offer valuable insights for educators who aim to enhance classroom experiences in software design courses.
{"title":"Common phenomena exhibited by students in their individual design processes: A multi-scenario case study on software design education","authors":"T. Fu;R. Sun;C. Li;L. Wang","doi":"10.23919/SAIEE.2025.10852572","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10852572","url":null,"abstract":"Cultivating students' software design capabilities through effective training has always been a challenge in software education. This paper is aimed at addressing this issue by adopting a multi-scenario case study approach to examine the independent design processes of 23 undergraduate students on an online teaching system. The selected case scenario models include transaction flow diagrams (TFDs), activity diagrams, sequence diagrams, and entity-relationship (ER) diagrams. By analyzing students' behavioral performances and design outcomes, a series of common phenomena are identified. These phenomena encompass common errors, such as overlooking key steps, struggling to distinguish similar data objects, and omitting critical entities or attributes. Common behaviors include offering various solutions, facing challenges in achieving specific design goals due to a lack of prior experience, and experiencing difficulties meeting requirements using prescribed syntax. Common approaches to assist students include providing reference software, adopting teamwork for idea generation, and allowing iterative modifications to improve outcomes. Based on these common phenomena exhibited by students, several recommendations are provided for software educators to enhance the development of students' software design capabilities, which mainly include considering students' prior experience in assignments, providing design references for unfamiliar software, encouraging peer discussions and multiple iterations, and guiding students towards continuous improvement rather than disregarding unconventional outcomes. The common phenomena identified in this paper seamlessly integrate with software design education, reflecting its distinct characteristics. Those common phenomena will help researchers understand student needs and challenges. Additionally, the research design, which analyzes student behaviors based on their software design outcomes, provides a fresh perspective in the field. Furthermore, the conclusions drawn in this paper offer valuable insights for educators who aim to enhance classroom experiences in software design courses.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 2","pages":"46-58"},"PeriodicalIF":1.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.23919/SAIEE.2025.10852566
U. Pillay;M. F. Khan
Green hydrogen has emerged as a prominent approach in an effort to mitigate global warming. Given the urgency of addressing the climate change crisis and mitigating global warming, there is an increasing emphasis on curtailing carbon dioxide emissions. This can be achieved through the production of green hydrogen. While there are various renewable power supply options capable of being utilized for producing green hydrogen, this research focuses solely on offshore wind, photovoltaics (PV), and a hybrid solution due to eThekwini's perceived abundance of solar radiation and extensive coastlines with ample wind resources, making it highly conducive to renewable energy generation. This paper initially introduces the concept of Green Hydrogen production and outlines the methodology employed to assess the viability of hydrogen production from various potential sources. Additionally, the energy requirements for a typical hydrogen plant are determined, along with a brief discussion on potential sites. In this case study, an energy yield assessment was conducted for PV, offshore wind, and a proposed hybrid model to determine the most beneficial source and identify any technical issues that may arise from the utilization of the considered power supplies. Finally, a high-level financial assessment assessing the project's financial viability is described.
{"title":"Hydrogen for a sustainable tomorrow: Evaluating the financial and technical dimensions of green hydrogen production in South Africa — A case study in Ethekwini muncipality","authors":"U. Pillay;M. F. Khan","doi":"10.23919/SAIEE.2025.10852566","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10852566","url":null,"abstract":"Green hydrogen has emerged as a prominent approach in an effort to mitigate global warming. Given the urgency of addressing the climate change crisis and mitigating global warming, there is an increasing emphasis on curtailing carbon dioxide emissions. This can be achieved through the production of green hydrogen. While there are various renewable power supply options capable of being utilized for producing green hydrogen, this research focuses solely on offshore wind, photovoltaics (PV), and a hybrid solution due to eThekwini's perceived abundance of solar radiation and extensive coastlines with ample wind resources, making it highly conducive to renewable energy generation. This paper initially introduces the concept of Green Hydrogen production and outlines the methodology employed to assess the viability of hydrogen production from various potential sources. Additionally, the energy requirements for a typical hydrogen plant are determined, along with a brief discussion on potential sites. In this case study, an energy yield assessment was conducted for PV, offshore wind, and a proposed hybrid model to determine the most beneficial source and identify any technical issues that may arise from the utilization of the considered power supplies. Finally, a high-level financial assessment assessing the project's financial viability is described.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 2","pages":"68-78"},"PeriodicalIF":1.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.23919/SAIEE.2025.10755059
N. Arish;M. J. Kamper;R. J. Wang
The shipping industry is shifting towards efficient and environmentally friendly propulsion systems to reduce costs and ecological impact. Traditional diesel engines, predominant in maritime operations, face challenges of high operating costs and environmental pollution, prompting the exploration of alternatives. Electric propulsion emerges as a promising solution, offering cost reduction and environmental preservation through reduced noise, This paper reviews electric ship propulsion systems and compares various marine propulsion systems, including in-hull, azimuth, and POD propulsion, with an emphasis on the latest POD systems. Specifically, it analyzes AZIPOD (ABB) and Mermaid (Rolls-Royce) propulsion systems in terms of motor type, cooling system, and power range. Additionally, the paper provides insights into the electrical components of POD propulsion, and the latest technology in ship propulsion, such as transformers, frequency converters, and propulsion motors, and explores redundancy in ship propulsion systems. It offers a detailed comparison of different electric motors, including DC motors, induction motors, superconducting motors, synchronous motors, and permanent magnet motors, discussing the advantages and disadvantages of each. This comprehensive review underscores the potential of electric propulsion systems to transform the maritime industry toward sustainability and efficiency.
航运业正在转向高效环保的推进系统，以降低成本和生态影响。传统的柴油发动机在海上作业中占主导地位，面临着运营成本高和环境污染的挑战，促使人们探索替代方案。本文回顾了电动船舶推进系统，并比较了各种船舶推进系统，包括船体内推进、方位推进和 POD 推进，重点介绍了最新的 POD 系统。具体而言，本文从电机类型、冷却系统和功率范围等方面分析了 AZIPOD（ABB）和美人鱼（罗尔斯-罗伊斯）推进系统。此外，论文还深入分析了 POD 推进系统的电气组件以及船舶推进系统的最新技术，如变压器、变频器和推进电机，并探讨了船舶推进系统的冗余问题。书中详细比较了不同的电机，包括直流电机、感应电机、超导电机、同步电机和永磁电机，讨论了每种电机的优缺点。这篇全面的评论强调了电力推进系统在改变海运业以实现可持续性和效率方面的潜力。
{"title":"Advancements in electrical marine propulsion technologies: A comprehensive overview","authors":"N. Arish;M. J. Kamper;R. J. Wang","doi":"10.23919/SAIEE.2025.10755059","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10755059","url":null,"abstract":"The shipping industry is shifting towards efficient and environmentally friendly propulsion systems to reduce costs and ecological impact. Traditional diesel engines, predominant in maritime operations, face challenges of high operating costs and environmental pollution, prompting the exploration of alternatives. Electric propulsion emerges as a promising solution, offering cost reduction and environmental preservation through reduced noise, This paper reviews electric ship propulsion systems and compares various marine propulsion systems, including in-hull, azimuth, and POD propulsion, with an emphasis on the latest POD systems. Specifically, it analyzes AZIPOD (ABB) and Mermaid (Rolls-Royce) propulsion systems in terms of motor type, cooling system, and power range. Additionally, the paper provides insights into the electrical components of POD propulsion, and the latest technology in ship propulsion, such as transformers, frequency converters, and propulsion motors, and explores redundancy in ship propulsion systems. It offers a detailed comparison of different electric motors, including DC motors, induction motors, superconducting motors, synchronous motors, and permanent magnet motors, discussing the advantages and disadvantages of each. This comprehensive review underscores the potential of electric propulsion systems to transform the maritime industry toward sustainability and efficiency.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 1","pages":"14-29"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.23919/SAIEE.2025.10755054
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2025.10755054","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10755054","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 1","pages":"2-2"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.23919/SAIEE.2025.10755055
{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2025.10755055","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10755055","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 1","pages":"41-41"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.23919/SAIEE.2025.10755051
Bathandekile M. Boshoma;Peter O. Olukanmi
Electricity demand continues to exceed supply in many sub-Saharan countries like South Africa, and frequent plant failures further reduce energy availability. To address this issue, it is essential to proactively predict plant failures and inform decisions on when to plan for outages. Given a myriad of prediction techniques, this study systematically analyzed various literature to provide a collective view of prediction approaches, their use cases, and context. Following the PRISMA guideline, relevant literature was searched using the Scopus database, and retrieved from the corresponding publisher sites. The selected studies focused on predicting the unplanned capability loss factor or the availability of power plants within the electricity industry domain. A thematic analysis was performed to identify emerging patterns related to current knowledge. Results revealed that prediction studies focus more on predicting availability than failure in coal-fired plants. The prediction horizon is mainly short-term, mostly in renewable plant. Artificial neural network, Bayesian analysis, and fuzzy rules are the prevalent technique found in most studies. Scholars and researchers can benefit from this study as it provided a simplified summary of power plant prediction techniques in a consolidated view.
{"title":"Prediction techniques for power plant failure and availability: A concise systematic review","authors":"Bathandekile M. Boshoma;Peter O. Olukanmi","doi":"10.23919/SAIEE.2025.10755051","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10755051","url":null,"abstract":"Electricity demand continues to exceed supply in many sub-Saharan countries like South Africa, and frequent plant failures further reduce energy availability. To address this issue, it is essential to proactively predict plant failures and inform decisions on when to plan for outages. Given a myriad of prediction techniques, this study systematically analyzed various literature to provide a collective view of prediction approaches, their use cases, and context. Following the PRISMA guideline, relevant literature was searched using the Scopus database, and retrieved from the corresponding publisher sites. The selected studies focused on predicting the unplanned capability loss factor or the availability of power plants within the electricity industry domain. A thematic analysis was performed to identify emerging patterns related to current knowledge. Results revealed that prediction studies focus more on predicting availability than failure in coal-fired plants. The prediction horizon is mainly short-term, mostly in renewable plant. Artificial neural network, Bayesian analysis, and fuzzy rules are the prevalent technique found in most studies. Scholars and researchers can benefit from this study as it provided a simplified summary of power plant prediction techniques in a consolidated view.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 1","pages":"30-39"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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