Comparison of Machine Learning and Statistical Methods in the Field of Renewable Energy Power Generation Forecasting - Mini Review

Aug 6, 2026

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Renewable energy is increasingly central to post-COVID-19 energy transition and climate-crisis response, yet its high intermittency and volatility threaten grid security and complicate power-system operation and scheduling. This mini review compares two widely used renewable power generation forecasting approaches: machine learning methods and statistical methods. It evaluates the strengths and limitations of each approach from multiple perspectives, then summarizes present challenges and feasible future research directions for renewable energy forecasting.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Background: post-COVID-19 transition and climate crisis

Need for renewable power forecasting

Method Comparison

Machine learning methods

Statistical methods

Discussion and Outlook

Advantages and disadvantages

Challenges and future research directions

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](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 field 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 field of renewable energy power generation forecasting: a mini review Yibo Dou 1, Shuwen Tan 2 and Dongwei Xie 3* 1School of Software, Xinjiang University, Urumqi, China, 2SJTU-UNIDO Joint Institute of Inclusive and Sustainable Industrial Development, Shanghai Jiao Tong University, Shanghai, China, 3School 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 efficiency. 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 different 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 The 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. The “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 ofthe pandemic on human society will persist for a longtime, climate change is also gaining more attention as another serious crisis. The 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 effects will belong-lasting, cumulative, and irreversible after a tipping point is reached (Jiao et al., 2020) . CO2 emissions from the power sector decreased significantly 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. The restructuring of global value chains in the post-COVID-19 era also notably brings new Frontiers in Energy Research 01 [frontiersin.org](frontiersin.org) opportunities for a transition to green and

FAQs

Why is renewable power generation forecasting important?

Renewable generation is highly intermittent and volatile, which threatens power-grid security and stability and hinders scheduling and operation. Forecasting helps integrate renewables and improve operational efficiency.

What two categories of forecasting methods does the mini review compare?

It compares machine learning methods and statistical methods for renewable power generation forecasting.

How does the mini review assess machine learning versus statistical approaches?

It discusses the advantages and disadvantages of both method types from different perspectives, followed by an overview of current challenges and feasible research directions.

Aug 6, 2026

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