TYPE Mini Review PUBLISHED 26 July 2023 DOI 10.3389/fenrg.2023.1218603 OPEN ACCESS EDITED BY Hugo Morais, University of Lisbon, Portugal REVIEWED BY Linfei Yin, Guangxi University, China *CORRESPONDENCE Dongwei Xie, 2020103615@ruc.edu.cn RECEIVED 07 May 2023 ACCEPTED 10 July 2023 PUBLISHED 26 July 2023 CITATION Dou Y, Tan S and Xie D (2023), Comparison of machine learning and statistical methods in the feld of renewable energy power generation forecasting: a mini review. Front. Energy Res. 11:1218603. doi: 10.3389/fenrg.2023.1218603 COPYRIGHT © 2023 Dou, Tan and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Comparison of machine learning and statistical methods in the feld of renewable energy power generation forecasting: a mini review Yibo Dou 1 , Shuwen Tan 2 and Dongwei Xie 3 * 1 School of Software, Xinjiang University, Urumqi, China, 2 SJTU-UNIDO Joint Institute of Inclusive and Sustainable Industrial Development, Shanghai Jiao Tong University, Shanghai, China, 3 School of Mathematics, Renmin University of China, Beijing, China In the post-COVID-19 era, countries are paying more attention to the energy transition as well as tackling the increasingly severe climate crisis. Renewable energy has attracted much attention because of its low economic costs and environmental friendliness. However, renewable energy cannot be widely adopted due to its high intermittency and volatility, which threaten the security and stability of power grids and hinder the operation and scheduling of power systems. Therefore, research on renewable power forecasting is important for integrating renewable energy and the power grid and improving operational efciency. In this mini-review, we compare two kinds of common renewable power forecasting methods: machine learning methods and statistical methods. Then, the advantages and disadvantages of the two methods are discussed from diferent perspectives. Finally, the current challenges and feasible research directions for renewable energy forecasting are listed. KEYWORDS power generation forecasting, machine learning, statistical methods, energy transition, climate crisis 1 Introduction Te COVID-19 pandemic had a huge impact on the world economy, society, and public health and was one of the most terrible disasters in human history. Te “post-COVID-19 era” is an era in which economic growth, international relations, industrial development, and people’s consumption habits have greatly changed due to the pandemic (Schwab and Malleret, 2020). While the impacts of the pandemic on human society will persist for a long time, climate change is also gaining more attention as another serious crisis. Te United Nations has listed climate change as a key issue in its recent Sustainable Development Goals (SDGs), which have been adopted into the 2030 Agenda (Usman et al., 2021). We can ascertain the reason: climate change can create catastrophic events, and its efects will be long-lasting, cumulative, and irreversible afer a tipping point is reached (Jiao et al., 2020). CO 2 emissions from the power sector decreased signifcantly during COVID-19, but this was largely due to the economic recession (Bertram et al., 2021). A green economic recovery in the post-COVID-19 era has prompted countries to think about the energy transition. Te restructuring of global value chains in the post-COVID-19 era also notably brings new Frontiers in Energy Research 01 frontiersin.org