COVID-19对风能与太阳能行业的影响及基于机器学习的能源成本预测

Aug 5, 2026

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This study examines how the COVID-19 pandemic affected renewable energy sectors across seven countries using techno-economic analysis and machine learning. Renewable outcomes varied by country: China saw lower renewable fractions and different COE drivers, while the USA increased renewable adoption through falling component costs and policy shifts. Canada shifted toward standalone systems, Germany reduced COE despite higher costs, and India lowered COE via expanded standalone HRESs. Japan remained stable, and Iran faced inflation pressures. ML forecasts indicate COE may rise in China and India due to pandemic effects.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

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Abstract

Introduction

COVID-19 spread and energy consumption disruption

Country-level impacts on electricity demand and renewables

Heliyon 10 (2024) e36662 Contents lists available at ScienceDirect Heliyon journal [homepage:](homepage: www.cell.com/heliyon)[ www.cell.com/heliyon](homepage: www.cell.com/heliyon) | Research article
COVID-19 impact on wind and solar energy sector and cost of energy prediction based on machine learning
Saheb Ghanbari Motlagha, b, Fatemeh Razi Astaraeia, * , Mohammad Montazeri a , Mohsen Bayatc
a Department of Renewable Energy Technologies and Energy Resources Engineering, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
b School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia c Electrical Engineering Department, University of Science and Technology of Mazandaran, Behshahr, Mazandaran, Iran | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Coronavirus disease of 2019 Hybrid renewable energy system Machine learning
Renewable energy
Techno-economic | | This study examines the impact of the COVID-19 pandemic on renewable energy sectors across seven countries through techno-economic analysis and machine learning (ML). In China, the renewable fraction decreased in grid-connected systems due to 14.6 % higher diesel fuel prices. They reduced grid electricity prices, with Cost of Energy (COE) reductions driven by a 2.8 % inflation decrease and a 3 % discount rate cut. The increase in renewable energy adoption in the USA during the pandemic was driven by decreased initial and operational costs of renewable components, a significant rise in diesel fuel prices, and government policy changes, despite a reduction in renewable energy sell-back prices and rising capital and annual costs due to expanded renewable capacity. Canada noted a shift to standalone systems with 50 % lower PV sell-back prices, 2 % lower WT prices, and a 48 % fuel cost rise, reducing COE except in grid/WT scenarios. Germany managed rising electricity and fuel costs, decreasing COE despite inflation. India expanded standalone HRESs driven by a sevenfold PV capacity increase, lowering COE. Japan saw stable COE with minimal variation. Iran faced economic challenges with a 104 % inflation increase, impacting COE despite a grid-connected COE decrease. Machine learning forecasts suggest that COVID-19 may cause an increase in COE in China and India due to pandemic effects. | 1. Introduction In December 2019, a novel virus was discovered in Wuhan, China [1,2]. This virus rapidly spread worldwide and evolved into a pandemic. Consequently, on January 30, 2020, the World Health Organization (WHO1) officially declared COVID-19 a global pandemic and public health emergency of international concern [3]. In a short period, COVID-19 caused widespread closure of public places, quarantine [4], and numerous fatalities [5]. According to WHO data, as of July 2022, more than 775 million cases ofCOVID-19 have been reported worldwide, with over 7 million fatalities [6]. The advent of the COVID-19 pandemic has profoundly impacted human life and government policies across various domains, * Corresponding author. [E-mail address:](E-mail address: Razias_m@ut.ac.ir)[ Razias_m@ut.ac.ir](E-mail address: Razias_m@ut.ac.ir) (F. Razi Astaraei). 1 Nomenclature table, including Abbreviations and Greek Symbols, can be found in Appendix A1. [https://doi.org/10.1016/j.heliyon.2024.e36662](https://doi.org/10.1016/j.heliyon.2024.e36662) Received 26 November 2023; Received in revised form 7 August 2024; Accepted 20 August 2024 Available online 24 August 2024 2405-8440/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license ([http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/)). particularly in the energy sector. Implementing quarantine measures and clo

FAQs

研究如何评估COVID-19对可再生能源的影响？

研究结合七国的技术-经济分析与机器学习预测，比较疫情期间的可再生能源表现及能源成本变化。

文章中哪些国家在疫情期间出现了显著的成本或采用变化？

中国、美国、加拿大、德国、印度、日本与伊朗都被讨论；其中美国提升了可再生采用，加拿大与印度在成本上出现明显变化。

机器学习预测对能源成本（COE）给出了怎样的结论？

机器学习预测显示，COVID-19可能导致中国和印度的COE上升，反映疫情效应对成本的影响。

Aug 5, 2026

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