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

Mingyu Li, Dongxiao Niu, Zhengsen Ji, Xiwen Cui and Lijie Sun
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Mingyu Li: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Zhengsen Ji: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Xiwen Cui: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Lijie Sun: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2021, vol. 13, issue 21, 1-19

Abstract: 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.

Keywords: offshore wind power investment; random forest; elastic net; regression forecast (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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