Evaluation and Forecasting of Wind Energy Investment Risk along the Belt and Road Based on a Novel Hybrid Intelligent Model
  • Liping Yan1,*, Wei-Chiang Hong2
1 Department of Economic Management, North China Electric Power University, Baoding, 071000, China
2 Department of Industrial and Business Management, Chang Gung University, Taoyuan, Taiwan
* Corresponding Author: Liping Yan. Email: ylp20212021@126.com
(This article belongs to this Special Issue:Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Received 11 March 2021; Accepted 13 May 2021 ; Published online 06 July 2021
Abstract
The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road. In order to obtain the scientific and real-time forecasting result, this paper constructs a novel hybrid intelligent model based on improved cloud model combined with GRA-TOPSIS and MBA-WLSSVM. Firstly, the factors influencing investment risk of wind energy along the Belt and Road are identified from three dimensions: endogenous risk, exogenous risk and process risk. Through the fuzzy threshold method, the final input index system is selected. Secondly, the risk evaluation method based on improved cloud model and GRA-TOPSIS is proposed. Thirdly, a modern intelligent model based on MBA-WLSSVM is designed. In modified bat algorithm (MBA), tent chaotic map is utilized to improve the basic bat algorithm, while weighted least squares support vector machine (WLSSVM) adopts wavelet kernel function to replace the traditional radial basis function to complete the model improvement. Finally, an example is given to verify the scientificity and accuracy of the model, which is helpful for investors to make fast and effective investment risk forecasting of wind energy along the Belt and Road. The example analysis proves that the proposed model can provide reference and basis for investment corpus to formulate the investment strategy in wind energy along the Belt and Road.
Keywords
The belt and road; wind power industry; investment risk evaluation; improved cloud model; GRA; TOPSIS; WLSSVM; MBA