[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125776-en":3,"doc-seo-125776-105":30,"detail-sidebar-cat-0-en-105":83},{"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},125776,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Renewable energy sources integration via machine learning modelling: A systematic literature review","Renewable energy sources (RESs) at the distribution level are increasingly attractive for cost and technology, but their stochastic nature (solar radiation, temperature, wind speed) creates significant uncertainty, causing power imbalance and reduced network stability. Managing and forecasting RES uncertainty is therefore essential for grid integration. Physical and statistical prediction models face limitations: high computational cost or restricted ability to address difficult forecasting scenarios. Machine learning methods learn from historical data and can handle non-uniform or noisy large datasets.","Heliyon 10 (2024) e26088  \nContents lists available at ScienceDirect  \nHeliyon  \njournal [homepage:](homepage: www.cell.com/heliyon)[ www.cell.com/heliyon](homepage: www.cell.com/heliyon)  \n| Research article\u003Cbr>Renewable energy sources integration via machine learning modelling: A systematic literature review\u003Cbr>Talal Alazemia, Mohamed Darwish a, *, Mohammed Radib\u003Cbr>a Brunel University London Kingston Lane Uxbridge, Middlesex, UB8 3PH, United Kingdom\u003Cbr>b UK Power Networks, Pocock House, 237 Southwark Bridge Rd, London, SE1 6NP, United Kingdom |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Renewable energy sources (RESs) Machine learning\u003Cbr>RES power output forecasting |  | The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources—solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided. |\n\n1. Introduction  \nIn recent years, the increasing need for decarbonising power systems has favoured the penetration of renewable energy sources (RESs), especially solar and wind energies, in the distribution grids. According to Ref. [1], over the last decade, the penetration of RESsin the power sector has remarkably increased in European countries, raising from 27 % to 57 % in Denmark, from 10 % to 26 % in Germany, from 15 % to 40 % in Spain and from 16 % to 44 % in Italy. Nevertheless, the transition to a higher penetration of RESs leads to several challenges in the power system [2–5]. In particular, since RESs depend on stochastic energy sources, they introduce a high  \n* Corresponding author.  \n[E-mail address:](E-mail address: Mohamed.Darwish@brunel.ac.uk)[ Mohamed.Darwish@brunel.ac.uk](E-mail address: Mohamed.Darwish@brunel.ac.uk) (M. Darwish).  \n[https://doi.org/10.1016/j.heliyon.2024.e26088](https://doi.org/10.1016/j.heliyon.2024.e26088)  \nReceived 30 April 2022; Received in revised form 25 January 2024; Accepted 7 February 2024 Available online 14 February 2024  \n2405-8440/© 2024 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)).  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