[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127472-en":3,"doc-seo-127472-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127472,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Literature review on forecasting green hydrogen production using machine learning and deep learning","Literature review on forecasting green hydrogen production using machine learning and deep learning, focusing on how AI supports the global energy transition through improved production forecasting. The review analyzes machine learning and deep learning algorithms applied to green hydrogen production prediction, synthesizing studies released between 2021 and March 2024. It summarizes prevailing trends and methods, evaluates the reported results, and highlights research gaps and future opportunities to guide further investigations in AI-driven hydrogen forecasting.","Literature review on forecasting green hydrogen production using machine learning and deep learning  \nMohamed Yassine Rhafes1, Omar Moussaoui1, Maria Simona Raboaca2  \n1MATSI Laboratory, High School of Technology (ESTO) , Mohammed First University, Oujda, Morocco 2Department ofICSI Energy, National Research and Development Institute for Cryogenics and Iso-topic Technologies,  \nRâmnicu Vâlcea, Romania  \nArticle history:  \nReceived Apr 14, 2024 Revised Nov 14, 2024 Accepted Nov 24, 2024  \nKeywords:  \nDeep learning Forecasting  \nGlobal energy transition Green hydrogen production Machine learning  \nCorresponding Author:  \nGreen hydrogen is a sustainable and clean energy source, for this purpose, it conducts the global energy transition. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) with the process of green hydrogen production is essential in enhancing its production. This literature review studies in detail the intersection between AI and green hydrogen. Firstly, it concentrates on ML and DL algorithms used in forecasting green hydrogen production. Secondly, it presents an analysis of the studies released from 2021 to March 2024. Finally, the focus is on the results realized by the ML and DL algorithms proposed by the studies reviewed. This study provides a summary that explains the trendsand methods used, as well as highlights the gaps and the opportunities in the field of AI and green hydrogen production. This liternature review presents a solid foundation for future research initiatives in this field.  \nThis is an open access article under the CC BY-SA license.  \nMohamed Yassine Rhafes  \nMATSI Laboratory, ESTO, Mohammed First University Oujda, Morocco [Email: mohamedyassine.rhafes@ump.ac.ma](Email: mohamedyassine.rhafes@ump.ac.ma)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nRenewable energy sources such as solar energy, wind energy, and others are becoming an important option in the energy sector [1], the process of producing green hydrogen starts with these sources that generate green electricity. Green hydrogen become an alternative to traditional fossil fuels due to the cleaner process of producing energy. The advantage of green hydrogen is that when it's used, it only produces water as a byproduct, unlike fossil fuels that produce dangerous gas emission [2], this quality makes it as an important element to build greener and sustainable future. Another important aspect is that green hydrogen can be stored for long periods with little energy loss [3] makes it a long-term energy solution.  \nProducing green hydrogen with electricity, especially using renewable energy sources, is a greener choice compared to old ways of making hydrogen. This fits with worldwide goals to use less fossil fuels and protect the environment. A great use of green hydrogen is to power electric cars [4], which are cleaner and more efficient than traditional vehicles. Other uses include power generation, heating, and various other applications [5] . To produce green hydrogen, the following three steps are essential as described in Figure 1. Renewable energy: the process begins with the generation of electricity from renewable energy sources [6],[7] for example, solar panels [8], wind turbines [9], or hydroelectric plants [10] . The electricity needs to come from renewable energy sources to confirm that hydrogen production is sustainable and does not emit greenhouse gases. Electrolysis process [11]–[14]: electrolysis involves splitting water (H2O) into its basic components, hydrogen (H2) and oxygen (O2) . This is achieved by applying an electrical current to water that  \nhas an electrolyte added to it, which helps in the conduction of electricity. The hydrogen gas collects at the cathode (the negative electrode), and oxygen gas collects at the anode (the positive electrode) . Green hydrogen output: the result is green hydrogen [15], [16] that can be used in various applications [5] .  \nHowever, t","cbCaifAuAWTGDzjm","https://ap.wps.com/l/cbCaifAuAWTGDzjm","pdf",505652,1,10,"English","en",105,"# Introduction\n## Green hydrogen in the energy transition\n## Production process and challenges\n# Background\n## Machine learning, deep learning, and statistical methods in forecasting","[{\"question\":\"Why is forecasting important for green hydrogen production?\",\"answer\":\"Forecasting helps address challenges such as weather unpredictability, varying regional operating conditions, and the complexity of modeling energy systems.\"},{\"question\":\"Which AI methods are emphasized in this review?\",\"answer\":\"The review emphasizes machine learning (ML) and deep learning (DL) algorithms used to forecast green hydrogen production, alongside statistical methods for background context.\"},{\"question\":\"What is the scope of the literature considered?\",\"answer\":\"The review analyzes studies published from 2021 through March 2024, summarizing trends, methods, and the results reported by the examined ML and DL approaches.\"}]","Literature review on forecasting green hydrogen production using machine learning and deep learning | 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is forecasting important for green hydrogen production?","Question",{"text":75,"@type":76},"Forecasting helps address challenges such as weather unpredictability, varying regional operating conditions, and the complexity of modeling energy systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which AI methods are emphasized in this review?",{"text":80,"@type":76},"The review emphasizes machine learning (ML) and deep learning (DL) algorithms used to forecast green hydrogen production, alongside statistical methods for background context.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the scope of the literature considered?",{"text":84,"@type":76},"The review analyzes studies published from 2021 through March 2024, summarizing trends, methods, and the results reported by the examined ML and DL 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