{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T08:23:38Z","timestamp":1762071818949,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T00:00:00Z","timestamp":1666396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Social Science Foundation of Jiangsu Province, China","award":["21GLC003"],"award-info":[{"award-number":["21GLC003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>In recent years, fossil energy reserves have decreased year by year, and the development and use of renewable energy has attracted great attention of governments all over the world. China continues to promote the high-quality development of renewable energy such as solar power generation. Accurate prediction of the share of solar power in China is beneficial to implementing the goals of carbon peaking and carbon neutralization. According to the website of China\u2019s National Bureau of statistics, the earliest annual data of China\u2019s solar power generation is 2017, which leads to there being very few data on the share of China\u2019s solar power generation. Therefore, the prediction accuracy of most prediction methods is low, and the advantages of the grey prediction model are shown. Based on the share of solar power in China from 2017 to 2020, this paper constructs an FGM (1,1) model, calculates r using the Particle Swarm Optimization (PSO) algorithm, and predicts the share of solar power in China in the next few years. r = 0.3858 and MAPE = 0.20% were obtained by calculation of the model. The prediction results show that the share of solar power generation in China will increase year by year, and it will reach about 4.2301% by 2030. In addition, it is found that the share of China\u2019s solar power generation in 2021 is 2.1520%, and the predicted value is 2.1906%. It can be seen that the prediction error is small. Finally, the limitations and future research directions are elucidated. The prediction results presented in this paper will help to guide the development of solar power generation in China, and are of great significance in speeding up the pace of energy structural adjustment, accelerating the construction of a clean, low-carbon, safe and efficient energy system, and promoting sustainable development.<\/jats:p>","DOI":"10.3390\/axioms11110581","type":"journal-article","created":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T20:43:50Z","timestamp":1666557830000},"page":"581","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of the Share of Solar Power in China Based on FGM (1,1) Model"],"prefix":"10.3390","volume":"11","author":[{"given":"Yuhan","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Management, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuya","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Management, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Information Management, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0170-1776","authenticated-orcid":false,"given":"Liusan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Information Management, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3604","DOI":"10.1080\/00036846.2021.1883543","article-title":"Valuing the greenhouse effect of political risks: The global case","volume":"53","author":"Dong","year":"2021","journal-title":"Appl. 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