Pub Date : 2025-07-14DOI: 10.1016/j.cles.2025.100205
Nayma Akther Jahan, Shahana Afrose Chowdhury, Haseeb Md. Irfanullah, Samiya Ahmed Selim
Bangladesh requires a huge amount of energy to keep its industries growing by using traditional fossil fuel options. The country has a huge potential for the rooftop solar PV systems (RSS) given its geographical location in the subtropical region, but the uptake of the RSS has not been satisfactory compared with the opportunity. There are studies on the potential rooftop area for installing the RSS, but no studies on the industries of Bangladesh from a user perspective. The present research identified the drivers and barriers to the RSS installation by interviewing representatives of different industries and using the technological acceptance model (TAM). It revealed that the emergence of the OPEX (operational expenditure) model, cost-effectiveness, energy security, and environmental awareness has driven the uptake of the RSS, whereas the upfront cost, bureaucracy, structural barrier, lack of information, and lack of financial incentives have demotivated the installation of the RSS. Financial incentives through policy adjustment, awareness building, and presenting best cases are recommended to motivate industries to adopt the RSS on a large scale.
{"title":"Uptake of solar energy by industries in Bangladesh: Driving factors, barriers, and opportunities","authors":"Nayma Akther Jahan, Shahana Afrose Chowdhury, Haseeb Md. Irfanullah, Samiya Ahmed Selim","doi":"10.1016/j.cles.2025.100205","DOIUrl":"10.1016/j.cles.2025.100205","url":null,"abstract":"<div><div>Bangladesh requires a huge amount of energy to keep its industries growing by using traditional fossil fuel options. The country has a huge potential for the rooftop solar PV systems (RSS) given its geographical location in the subtropical region, but the uptake of the RSS has not been satisfactory compared with the opportunity. There are studies on the potential rooftop area for installing the RSS, but no studies on the industries of Bangladesh from a user perspective. The present research identified the drivers and barriers to the RSS installation by interviewing representatives of different industries and using the technological acceptance model (TAM). It revealed that the emergence of the OPEX (operational expenditure) model, cost-effectiveness, energy security, and environmental awareness has driven the uptake of the RSS, whereas the upfront cost, bureaucracy, structural barrier, lack of information, and lack of financial incentives have demotivated the installation of the RSS. Financial incentives through policy adjustment, awareness building, and presenting best cases are recommended to motivate industries to adopt the RSS on a large scale.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-05DOI: 10.1016/j.cles.2025.100200
Dalal Bamufleh , Yong Wang , A. Rammohan , Tao Yang
This paper provides a comprehensive review of Energy Storage System (ESS) supply chain modeling and optimization over the past decade (2014–2024). Motivated by the increasing demand for ESS integration with renewable energy sources and the complexities of battery energy storage systems (BESSs), this study employs a systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The review results indicated that multi-objective optimization models dominate ESS and BESS supply chain studies, due to their capability to manage the trade-offs between these chains' economic performance, environmental sustainability, and operational efficiency. The analysis identifies China's dominance in ESS research because of the Chinese government's extensive investments in renewable energy and electric vehicle (EV) production and characterizes 2019 as the most productive year for publications, given the global legislative changes and technological advancements. The review recognizes the future direction of ESS research related to integrating multiple optimization techniques, optimizing ESS supply chain environmental impacts, hybrid renewable ESSs, and shared ESSs. Also, it emphasizes the growing significance of artificial intelligence (AI), machine learning (ML), and deep reinforcement learning (DRL), as emerging methodologies for improving ESS supply chain optimization. This review paper contributes to the literature by providing practical insights related to ESS supply chain optimization, aligning with global decarbonization targets, and highlighting ESSs' future research approaches. Policymakers, manufacturers, energy providers, and researchers can utilize these findings to design sustainable ESS supply chains that optimize costs, environmental impacts, and social aspects.
{"title":"Energy storage supply chain modeling and optimization: A systematic review","authors":"Dalal Bamufleh , Yong Wang , A. Rammohan , Tao Yang","doi":"10.1016/j.cles.2025.100200","DOIUrl":"10.1016/j.cles.2025.100200","url":null,"abstract":"<div><div>This paper provides a comprehensive review of Energy Storage System (ESS) supply chain modeling and optimization over the past decade (2014–2024). Motivated by the increasing demand for ESS integration with renewable energy sources and the complexities of battery energy storage systems (BESSs), this study employs a systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The review results indicated that multi-objective optimization models dominate ESS and BESS supply chain studies, due to their capability to manage the trade-offs between these chains' economic performance, environmental sustainability, and operational efficiency. The analysis identifies China's dominance in ESS research because of the Chinese government's extensive investments in renewable energy and electric vehicle (EV) production and characterizes 2019 as the most productive year for publications, given the global legislative changes and technological advancements. The review recognizes the future direction of ESS research related to integrating multiple optimization techniques, optimizing ESS supply chain environmental impacts, hybrid renewable ESSs, and shared ESSs. Also, it emphasizes the growing significance of artificial intelligence (AI), machine learning (ML), and deep reinforcement learning (DRL), as emerging methodologies for improving ESS supply chain optimization. This review paper contributes to the literature by providing practical insights related to ESS supply chain optimization, aligning with global decarbonization targets, and highlighting ESSs' future research approaches. Policymakers, manufacturers, energy providers, and researchers can utilize these findings to design sustainable ESS supply chains that optimize costs, environmental impacts, and social aspects.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-03DOI: 10.1016/j.cles.2025.100199
Michael Osezua, Olusegun S. Tomomewo
The evolution of policies and regulations supporting battery energy storage system (BESS) development, utilization, and sustainability to enhance resource adequacy was investigated. The study examined the role of BESS in mitigating renewable energy intermittency, using China, Japan, and South Korea as case studies. The review finds that environmental, economic, political, technological, and regulatory factors significantly influence BESS applications' viability, growth, and sustainability. BESS offers environmental and social benefits but faces challenges like raw material price volatility and supply chain disruptions. The study concludes that integrating renewable energy sources and the growing demand for grid stability will continue to drive BESS adoption. However, supply chain challenges, international green trade barriers, and evolving technologies will shape the next phase of BESS growth. Collaboration among stakeholders, strategic partnerships, technological innovation, and supportive policies are required to advance the global adoption of BESS. The study highlights critical policy frameworks facilitating BESS deployment while ensuring grid stability and sustainability.
{"title":"Advancing grid stability and renewable energy: Policy evolution of battery energy storage systems in China, Japan, and South Korea","authors":"Michael Osezua, Olusegun S. Tomomewo","doi":"10.1016/j.cles.2025.100199","DOIUrl":"10.1016/j.cles.2025.100199","url":null,"abstract":"<div><div>The evolution of policies and regulations supporting battery energy storage system (BESS) development, utilization, and sustainability to enhance resource adequacy was investigated. The study examined the role of BESS in mitigating renewable energy intermittency, using China, Japan, and South Korea as case studies. The review finds that environmental, economic, political, technological, and regulatory factors significantly influence BESS applications' viability, growth, and sustainability. BESS offers environmental and social benefits but faces challenges like raw material price volatility and supply chain disruptions. The study concludes that integrating renewable energy sources and the growing demand for grid stability will continue to drive BESS adoption. However, supply chain challenges, international green trade barriers, and evolving technologies will shape the next phase of BESS growth. Collaboration among stakeholders, strategic partnerships, technological innovation, and supportive policies are required to advance the global adoption of BESS. The study highlights critical policy frameworks facilitating BESS deployment while ensuring grid stability and sustainability.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cleaner and sustainable Photovoltaic (PV) systems need to be supervised and monitored to reduce waste energy and improve power efficiency. The proposed technique in this work enhances solar energy production by precise fault detection of short-circuit and partial shading. It extends the PV system lifespan by mitigation component and further premature replacements. Moreover, automatic fault diagnosis helps maintain steady performance in variable climatic conditions and under varying occurred faults that minimize the backup to generators and energy losses. Firstly, we introduce a Bonobo Optimization Algorithm (BOA) that is capable of extracting and identifying the unknown parameters of the PV cell to model our study PV system and to mimic the fault behaviors. The identified model is validated and then used to generate the I-V and P-V curves, which are then fed to three autoencoders (AE) within an unsupervised learning framework to extract their features. Afterward, reinforcement learning (RL) is integrated through a stacked autoencoder (SAE) to combine environmental attributes such as solar irradiance and temperature with electrical features to improve the learned features and their sparsity. Also, to enable the system to adapt dynamically to new fault scenarios and noisy environments, deep-reinforcement learning (DRL) improves feature representation and classification through Artificial Neural Networks (ANN). This methodology provides an identification and categorization of 12 selected fault types in separated and combined ways, where this technique has been applied to a PV plant located in Algeria. The classification results exhibited exceptional accuracy, achieving 100% in the training phase and 99.8% in the testing phase, even amongst noisy input conditions with 97.2%. This study provides valuable insights into improving the reliability and efficiency of PV systems, particularly in the smart IV diagnosis that used multi-string PV inverter.
{"title":"Efficient Deep-Reinforcement Learning for Photovoltaic Systems Under Faults Based on the I-V Curve Approach","authors":"YETTOU Tariq , SEGHIOUR Abdellatif , BOUCHETATA Nadir , BENOUZZA Noureddine , MOSTEFAOUI Imene Meriem , RABHI Abdelhamid , Santiago Silvestre , CHOUDER Aissa","doi":"10.1016/j.cles.2025.100197","DOIUrl":"10.1016/j.cles.2025.100197","url":null,"abstract":"<div><div>Cleaner and sustainable Photovoltaic (PV) systems need to be supervised and monitored to reduce waste energy and improve power efficiency. The proposed technique in this work enhances solar energy production by precise fault detection of short-circuit and partial shading. It extends the PV system lifespan by mitigation component and further premature replacements. Moreover, automatic fault diagnosis helps maintain steady performance in variable climatic conditions and under varying occurred faults that minimize the backup to generators and energy losses. Firstly, we introduce a Bonobo Optimization Algorithm (BOA) that is capable of extracting and identifying the unknown parameters of the PV cell to model our study PV system and to mimic the fault behaviors. The identified model is validated and then used to generate the I-V and P-V curves, which are then fed to three autoencoders (AE) within an unsupervised learning framework to extract their features. Afterward, reinforcement learning (RL) is integrated through a stacked autoencoder (SAE) to combine environmental attributes such as solar irradiance and temperature with electrical features to improve the learned features and their sparsity. Also, to enable the system to adapt dynamically to new fault scenarios and noisy environments, deep-reinforcement learning (DRL) improves feature representation and classification through Artificial Neural Networks (ANN). This methodology provides an identification and categorization of 12 selected fault types in separated and combined ways, where this technique has been applied to a PV plant located in Algeria. The classification results exhibited exceptional accuracy, achieving 100% in the training phase and 99.8% in the testing phase, even amongst noisy input conditions with 97.2%. This study provides valuable insights into improving the reliability and efficiency of PV systems, particularly in the smart IV diagnosis that used multi-string PV inverter.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-25DOI: 10.1016/j.cles.2025.100195
Kazuma Murakami , Ikuho Kochi
Electricity accounts for 65.3 % of household CO2 emissions in Japan; therefore, more household energy conservation is needed. This study examines the effects of information provision on various household energy-saving behaviors using randomized controlled trials (RCT). For Japanese consumers who have recently become free to choose their electricity provider, we examine two types of information provision with the same economic incentives but different framing: information on the Past - information about historical changes in electricity bills for the average household of their electricity provider–and information on Others - information about differences in electricity bills for the average household of different electricity providers. We collected objective measures of household electricity consumption levels through meter readings and subjective measures of behavioral changes through a questionnaire. Our results show that information on the Past has more impact on reducing electricity consumption for households with a higher volume of electricity consumption than others. The channels for this reduction are the behaviors of “not leaving the air conditioner on,” a constant time-consuming behavior, and “lowering the refrigerator's internal temperature,” a hassle-free one-time behavior. Information on the Past can be a low-cost and proactive information-provision measure for non-profit organizations and local governments.
{"title":"Providing electricity price information to households and reducing electricity consumption: Results from a field experiment in Japan","authors":"Kazuma Murakami , Ikuho Kochi","doi":"10.1016/j.cles.2025.100195","DOIUrl":"10.1016/j.cles.2025.100195","url":null,"abstract":"<div><div>Electricity accounts for 65.3 % of household CO<sub>2</sub> emissions in Japan; therefore, more household energy conservation is needed. This study examines the effects of information provision on various household energy-saving behaviors using randomized controlled trials (RCT). For Japanese consumers who have recently become free to choose their electricity provider, we examine two types of information provision with the same economic incentives but different framing: <em>information on the Past -</em> information about historical changes in electricity bills for the average household of their electricity provider–and <em>information on Others -</em> information about differences in electricity bills for the average household of different electricity providers. We collected objective measures of household electricity consumption levels through meter readings and subjective measures of behavioral changes through a questionnaire. Our results show that <em>information on the Past</em> has more impact on reducing electricity consumption for households with a higher volume of electricity consumption than others. The channels for this reduction are the behaviors of “not leaving the air conditioner on,” a constant time-consuming behavior, and “lowering the refrigerator's internal temperature,” a hassle-free one-time behavior. <em>Information on the Past</em> can be a low-cost and proactive information-provision measure for non-profit organizations and local governments.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-25DOI: 10.1016/j.cles.2025.100198
Jun Liu , Shenghao Liu , Mingxiang Li , Xueying Wu , Zhengwei Li , Xia Hao
In China, the current energy consumption and pollution levels of oilfield are not in line with green development trends. Consequently, it is essential to integrate traditional oil/gas exploitation with renewable energy, like photovoltaic power. This paper provides an overview of the application of Distributed Photovoltaic Systems (DPVS) in oil-gas field. China's escalating energy demand and environmental concerns have underscored the significance of renewable energy, particularly photovoltaics. It also addresses the environmental impact of oilfield extraction, highlighting the necessity to reduce CO2 emissions. By analyzing policies that promote renewable energy, the paper identifies the DPVS potential in alleviating environmental issues. The paper examines the key elements and development status of photovoltaic systems for oil-gas fields, encompassing their history, components, and technologies. It explores the power consumption and system characteristics of oil-gas fields, proposing a structured methodology for designing and planning DPVS -including feasibility analysis, equipment selection, and cost calculations. Additionally, the paper assesses the economic benefits of DPVS using indicators such as Net Present Value, Internal Rate of Return, Dynamic Payback Period, and Levelized Cost of Energy. Finally, the paper discusses the limitations of DPVS in oilfields and outlines future trends, including solar-wind hybrid systems, DC microgrids, and integrated energy systems.
{"title":"Application of the distributed photovoltaic systems towards oil-gas field and its implications for carbon emission reduction in China: A review on current and novel perspective on engineering approaches","authors":"Jun Liu , Shenghao Liu , Mingxiang Li , Xueying Wu , Zhengwei Li , Xia Hao","doi":"10.1016/j.cles.2025.100198","DOIUrl":"10.1016/j.cles.2025.100198","url":null,"abstract":"<div><div>In China, the current energy consumption and pollution levels of oilfield are not in line with green development trends. Consequently, it is essential to integrate traditional oil/gas exploitation with renewable energy, like photovoltaic power. This paper provides an overview of the application of Distributed Photovoltaic Systems (DPVS) in oil-gas field. China's escalating energy demand and environmental concerns have underscored the significance of renewable energy, particularly photovoltaics. It also addresses the environmental impact of oilfield extraction, highlighting the necessity to reduce CO<sub>2</sub> emissions. By analyzing policies that promote renewable energy, the paper identifies the DPVS potential in alleviating environmental issues. The paper examines the key elements and development status of photovoltaic systems for oil-gas fields, encompassing their history, components, and technologies. It explores the power consumption and system characteristics of oil-gas fields, proposing a structured methodology for designing and planning DPVS -including feasibility analysis, equipment selection, and cost calculations. Additionally, the paper assesses the economic benefits of DPVS using indicators such as Net Present Value, Internal Rate of Return, Dynamic Payback Period, and Levelized Cost of Energy. Finally, the paper discusses the limitations of DPVS in oilfields and outlines future trends, including solar-wind hybrid systems, DC microgrids, and integrated energy systems.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study quantifies the operational carbon footprint of the Renault Kwid E-Tech (electric vehicle) and Renault Kwid Intense flex (gasoline and ethanol internal combustion engine vehicle) under a Well-to-Wheel approach within the Brazilian context. With a functional unit of 100,000 km, this analysis evaluates greenhouse gas (GHG) emissions associated with fuel consumption and considers different electric mixes across Brazilian regions, along with the periodic maintenance of each vehicle type. The results reveal significant environmental benefits in regions such as the Northeast, where renewable energy sources predominate, reducing the carbon footprint of the electric model, with a carbon footprint of 0.071 kg CO2-eq/kWh. By contrast, the higher carbon intensity of the South’s electricity mix reliant on coal, with a carbon footprint of 0.281 kg CO2-eq/kWh, presents limitations in achieving emissions reductions with electric vehicles. Ethanol, a renewable biofuel in the Brazilian market, demonstrated a 46 % reduction in GHG emissions compared to gasoline. This study contributes to the sustainable mobility discourse, highlighting the critical role of regional energy sources, fuel choices, and sustainable production practices in emissions outcome. These insights support the development of policies encouraging cleaner energy matrices and biofuel use, contributing to Brazil's emissions reduction goals.
该研究量化了雷诺Kwid E-Tech（电动汽车）和雷诺Kwid Intense flex（汽油和乙醇内燃机汽车）在巴西的井到轮方法下的运行碳足迹。以10万公里的功能单元为例，该分析评估了与燃料消耗相关的温室气体（GHG）排放，并考虑了巴西地区不同的电力混合，以及每种车型的定期维护。结果表明，在可再生能源占主导地位的东北等地区，显著的环境效益减少了电动汽车的碳足迹，碳足迹为0.071 kg CO2-eq/kWh。相比之下，南方电力结构的碳强度较高，依赖煤炭，碳足迹为0.281千克二氧化碳当量/千瓦时，这对实现电动汽车的减排提出了限制。乙醇是巴西市场上的一种可再生生物燃料，与汽油相比，它的温室气体排放量减少了46%。本研究为可持续交通话语做出了贡献，强调了区域能源、燃料选择和可持续生产实践在排放结果中的关键作用。这些见解支持制定鼓励使用清洁能源和生物燃料的政策，有助于巴西实现减排目标。
{"title":"Comparative operational carbon footprints of a vehicle in Brazil: Electric, ethanol, and gasoline","authors":"João Marcelo Fernandes Gualberto Galiza , Silvia Guillén-Lambea , Monica Carvalho","doi":"10.1016/j.cles.2025.100194","DOIUrl":"10.1016/j.cles.2025.100194","url":null,"abstract":"<div><div>This study quantifies the operational carbon footprint of the Renault Kwid E-Tech (electric vehicle) and Renault Kwid Intense <em>flex</em> (gasoline and ethanol internal combustion engine vehicle) under a Well-to-Wheel approach within the Brazilian context. With a functional unit of 100,000 km, this analysis evaluates greenhouse gas (GHG) emissions associated with fuel consumption and considers different electric mixes across Brazilian regions, along with the periodic maintenance of each vehicle type. The results reveal significant environmental benefits in regions such as the Northeast, where renewable energy sources predominate, reducing the carbon footprint of the electric model, with a carbon footprint of 0.071 kg CO<sub>2</sub>-eq/kWh. By contrast, the higher carbon intensity of the South’s electricity mix reliant on coal, with a carbon footprint of 0.281 kg CO<sub>2</sub>-eq/kWh, presents limitations in achieving emissions reductions with electric vehicles. Ethanol, a renewable biofuel in the Brazilian market, demonstrated a 46 % reduction in GHG emissions compared to gasoline. This study contributes to the sustainable mobility discourse, highlighting the critical role of regional energy sources, fuel choices, and sustainable production practices in emissions outcome. These insights support the development of policies encouraging cleaner energy matrices and biofuel use, contributing to Brazil's emissions reduction goals.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-14DOI: 10.1016/j.cles.2025.100193
Armin Tayefeh, Alireza Aslani, Rahim Zahedi, Hossein Yousefi
{"title":"Corrigendum to “Reducing energy consumption in a factory and providing an upgraded energy system to improve energy performance” [Cleaner Energy Systems, Volume 8, August 2024, 100124]","authors":"Armin Tayefeh, Alireza Aslani, Rahim Zahedi, Hossein Yousefi","doi":"10.1016/j.cles.2025.100193","DOIUrl":"10.1016/j.cles.2025.100193","url":null,"abstract":"","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"11 ","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal).
This article has been retracted at the request of the Editor-in-Chief.
Post publication the editor found that several citations were added to this paper which are not relevant to the topic of the paper. In addition, it was found that the data utilized in the study were poorly presented, referenced, and described, making it difficult for readers to fully understand and evaluate the findings. The method section was also found to be insufficiently clear, lacking the necessary detail required for replication or validation of the results.
An Expression of Concern was appended to the paper whilst the authors were given a chance to write a revised version of their original article. Despite substantial efforts by the authors, there remain significant unresolved issues that compromise the integrity and reproducibility of the study.
Subsequent evaluation of the revised paper has concluded that it does not advance understanding of the topic. The changes needed were judged to exceed the threshold that could be corrected via a Corrigendum and therefore necessitated retraction. This retraction supersedes the Expression of Concern.
The paper will be resubmitted, and additional measures will be implemented to ensure that the methodologies described, and the source of the data are clearer. References will also be aligned with the context of the article. Once the resubmitted paper undergoes review and, if accepted for publication, a link to the new article will be provided here for reference.
{"title":"Retraction notice to “Forecasting Solar Energy generation in the Mediterranean Region up to 2030-2050 Using Convolutional Neural Networks (CNN)” [Cleaner Energy Systems 10 (2025) 100167]","authors":"Mahmood Abdoos , Hamidreza Rashidi , Pourya Esmaeili , Hossein Yousefi , Mohammad Hossein Jahangir","doi":"10.1016/j.cles.2025.100192","DOIUrl":"10.1016/j.cles.2025.100192","url":null,"abstract":"<div><div>This article has been retracted: please see Elsevier Policy on Article Withdrawal (<span><span>https://www.elsevier.com/about/policies/article-withdrawal</span><svg><path></path></svg></span>).</div><div>This article has been retracted at the request of the Editor-in-Chief.</div><div>Post publication the editor found that several citations were added to this paper which are not relevant to the topic of the paper. In addition, it was found that the data utilized in the study were poorly presented, referenced, and described, making it difficult for readers to fully understand and evaluate the findings. The method section was also found to be insufficiently clear, lacking the necessary detail required for replication or validation of the results.</div><div>An Expression of Concern was appended to the paper whilst the authors were given a chance to write a revised version of their original article. Despite substantial efforts by the authors, there remain significant unresolved issues that compromise the integrity and reproducibility of the study.</div><div>Subsequent evaluation of the revised paper has concluded that it does not advance understanding of the topic. The changes needed were judged to exceed the threshold that could be corrected via a Corrigendum and therefore necessitated retraction. This retraction supersedes the Expression of Concern.</div><div>The paper will be resubmitted, and additional measures will be implemented to ensure that the methodologies described, and the source of the data are clearer. References will also be aligned with the context of the article. Once the resubmitted paper undergoes review and, if accepted for publication, a link to the new article will be provided here for reference.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"11 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-16DOI: 10.1016/j.cles.2025.100190
Eugene Haochen Yu , Yuan Yuan , Chinhao Chong , Maximilian Arras , Linwei Ma , Zheng Li , Weidou Ni
In 2020, the heat demand drove 54 % of the energy-related carbon emissions (ERCEs) in China’s industry, and the majority of the heat demand was in manufacturing. Due to the scale, numerous sub-sectors, and complex production processes of the manufacturing industry, together with insufficient data availability, a lack of comprehensive data for heat demand differentiating sub-sectors and temperature zones still exists. This study developed a four-step accounting method to fill this gap, including the selection of sub-sectors, identification of typical production processes, estimation of heat demand by temperature zones for each process, and calculation of the total heat demand by sub-sectors and temperature zones. 9 manufacturing sub-sectors were selected to estimate the heat demand between 0 and 1800 °C, and 16 production processes were identified to differentiate the heat demand by temperature zones. The results indicated that the temperature zones of 1601–1800 °C, 0–200 °C and 801–1000 °C account for 28.0 %, 20.4 % and 19.6 % of the total heat demand, respectively. Meanwhile, the high temperature zone was dominated by ferrous metals and non-metallics, the middle temperature zone was dominated by chemicals, ferrous metals, and non-ferrous metals, and the low temperature zone was diverse among all sub-sectors.
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