Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

Recently, countries around the world have begun to develop low-carbon energy sources to alleviate energy shortage and cope with climate change. The offshore wind power has become a new direction for clean energy exploration. However, the accuracy of offshore wind power investment is still an urgent...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mingyu Li, Dongxiao Niu, Zhengsen Ji, Xiwen Cui, Lijie Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	offshore wind power investment random forest elastic net regression forecast Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/28068234055d4e5a88907d4052aef071
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:28068234055d4e5a88907d4052aef071
record_format	dspace
spelling	oai:doaj.org-article:28068234055d4e5a88907d4052aef0712021-11-11T19:49:28ZForecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net10.3390/su1321122622071-1050https://doaj.org/article/28068234055d4e5a88907d4052aef0712021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12262https://doaj.org/toc/2071-1050Recently, countries around the world have begun to develop low-carbon energy sources to alleviate energy shortage and cope with climate change. The offshore wind power has become a new direction for clean energy exploration. However, the accuracy of offshore wind power investment is still an urgent problem due to its complexity. Therefore, this paper investigates offshore wind power investment to improve the investment forecasting accuracy. In this study, the random forest (RF) algorithm was used to screen out the key factors influencing multi-dimensional global offshore wind power investment, and the elastic net (EN) was optimized using the ADMM algorithm and used in the global offshore wind power investment forecast model. The results show that the adoption of the random forest algorithm can effectively screen out the key influencing factors of global offshore wind power investment. Water depth, offshore distance and sweeping area have the most influence on the investment. Moreover, compared with other models, the elastic net optimized by ADMM can better reflect the changing trend of global offshore wind power investment, with smaller errors and a higher regression accuracy. The application of the RF–EN combined model can screen out effective factors from complex multi-dimensional influencing factors, and perform high-precision regression analysis, which is conducive to improving the global offshore wind power investment forecast. The conclusion obtained can set a more reasonable plan for the future construction and investment of global offshore wind power projects.Mingyu LiDongxiao NiuZhengsen JiXiwen CuiLijie SunMDPI AGarticleoffshore wind power investmentrandom forestelastic netregression forecastEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12262, p 12262 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	offshore wind power investment random forest elastic net regression forecast Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	offshore wind power investment random forest elastic net regression forecast Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Mingyu Li Dongxiao Niu Zhengsen Ji Xiwen Cui Lijie Sun Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net
description	Recently, countries around the world have begun to develop low-carbon energy sources to alleviate energy shortage and cope with climate change. The offshore wind power has become a new direction for clean energy exploration. However, the accuracy of offshore wind power investment is still an urgent problem due to its complexity. Therefore, this paper investigates offshore wind power investment to improve the investment forecasting accuracy. In this study, the random forest (RF) algorithm was used to screen out the key factors influencing multi-dimensional global offshore wind power investment, and the elastic net (EN) was optimized using the ADMM algorithm and used in the global offshore wind power investment forecast model. The results show that the adoption of the random forest algorithm can effectively screen out the key influencing factors of global offshore wind power investment. Water depth, offshore distance and sweeping area have the most influence on the investment. Moreover, compared with other models, the elastic net optimized by ADMM can better reflect the changing trend of global offshore wind power investment, with smaller errors and a higher regression accuracy. The application of the RF–EN combined model can screen out effective factors from complex multi-dimensional influencing factors, and perform high-precision regression analysis, which is conducive to improving the global offshore wind power investment forecast. The conclusion obtained can set a more reasonable plan for the future construction and investment of global offshore wind power projects.
format	article
author	Mingyu Li Dongxiao Niu Zhengsen Ji Xiwen Cui Lijie Sun
author_facet	Mingyu Li Dongxiao Niu Zhengsen Ji Xiwen Cui Lijie Sun
author_sort	Mingyu Li
title	Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net
title_short	Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net
title_full	Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net
title_fullStr	Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net
title_full_unstemmed	Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net
title_sort	forecast research on multidimensional influencing factors of global offshore wind power investment based on random forest and elastic net
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/28068234055d4e5a88907d4052aef071
work_keys_str_mv	AT mingyuli forecastresearchonmultidimensionalinfluencingfactorsofglobaloffshorewindpowerinvestmentbasedonrandomforestandelasticnet AT dongxiaoniu forecastresearchonmultidimensionalinfluencingfactorsofglobaloffshorewindpowerinvestmentbasedonrandomforestandelasticnet AT zhengsenji forecastresearchonmultidimensionalinfluencingfactorsofglobaloffshorewindpowerinvestmentbasedonrandomforestandelasticnet AT xiwencui forecastresearchonmultidimensionalinfluencingfactorsofglobaloffshorewindpowerinvestmentbasedonrandomforestandelasticnet AT lijiesun forecastresearchonmultidimensionalinfluencingfactorsofglobaloffshorewindpowerinvestmentbasedonrandomforestandelasticnet
_version_	1718431397896519680

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

Ejemplares similares