[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118200-en":3,"doc-seo-118200-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118200,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning's Role in Achieving Global Net Zero Emissions - Article","This research investigates the application of machine learning models to optimize renewable energy systems and support global Net Zero emissions targets. The study evaluates how machine learning improves energy forecasting, grid management, and storage optimization to increase reliability and efficiency of renewable sources. Models including LSTM, Random Forest, SVM, and ARIMA predict generation and demand patterns, assessed via MAE and RMSE. Results show measurable error reductions and grid efficiency gains, alongside quantified CO2 emission decreases by energy source.","University of Dundee  \nMachine Learning's Role in Achieving Global Net Zero Emissions  \nOladapo, Bankole; Olawumi, Mattew A. ; Omigbodun, Francis T.  \nDOI:  \n10.3390/atmos15101250  \nPublication date:  \n2024  \nLicence: CC BY  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in Discovery Research Portal  \nCitation for published version (APA):  \nOladapo, B. , Olawumi, M. A. , & Omigbodun, F. T. (2024) . Machine Learning's Role in Achieving Global Net Zero Emissions. Atmosphere, 15(10), Article 1250. [https://doi.org/10.3390/atmos15101250](https://doi.org/10.3390/atmos15101250)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 27. Oct. 2024  \n atmosphere  \nArticle  \nMachine Learning for Optimising Renewable Energy and Grid Efficiency  \nBankole I. Oladapo 1, *, Mattew A. Olawumi 2 and Francis T. Omigbodun 3  \nCitation: Oladapo, B.I.; Olawumi, M.A.; Omigbodun, F.T. Machine Learning for Optimising Renewable Energy and Grid Efficiency. Atmosphere 2024, 15, 1250 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)atmos15101250  \nAcademic Editors: Domenico Toscano and Grazia Fattoruso  \nReceived: 21 September 2024  \nRevised: 13 October 2024  \nAccepted: 16 October 2024  \nPublished: 19 October 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK  \n2 Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK; [olawumisola13@gmail.com](olawumisola13@gmail.com)  \n3 Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK; [f.omigbdun@lboro.ac.uk](f.omigbdun@lboro.ac.uk)  \n* [Correspondence: p17243433@my365.dmu.ac.uk](Correspondence: p17243433@my365.dmu.ac.uk)  \nAbstract: This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long ShortTerm Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) . Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5% . The study quantified CO 2 emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet","cbCainzvmDbPhNRK","https://ap.wps.com/l/cbCainzvmDbPhNRK","pdf",3817113,1,21,"English","en",105,"# Introduction\n## Net Zero emissions and climate mitigation\n## Machine learning for energy system optimization\n# Methods (Machine learning models)\n## LSTM, Random Forest, SVM, and ARIMA\n## Evaluation metrics: MAE and RMSE\n# Key Results\n## Grid efficiency and battery storage improvements\n## CO2 emission reductions by energy source\n# Conclusion\n## Implications for Net Zero targets","[{\"question\":\"What is the main goal of this study on machine learning and Net Zero?\",\"answer\":\"The study aims to evaluate how machine learning can improve renewable energy forecasting, grid management, and storage optimization to contribute to achieving Net Zero emissions targets.\"},{\"question\":\"Which machine learning models are used to predict energy generation and demand?\",\"answer\":\"The research applies LSTM, Random Forest, SVM, and ARIMA to forecast energy generation and demand patterns.\"},{\"question\":\"How is model performance evaluated in the article?\",\"answer\":\"Performance is assessed using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), including reported error reduction results.\"}]","Machine Learning's Role in Achieving Global Net Zero Emissions - 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