Energy-Related CO 2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm

Haoran Zhao, Sen Guo and Huiru Zhao
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Haoran Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Sen Guo: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Huiru Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Energies, 2017, vol. 10, issue 7, 1-15

Abstract: Accurate and reliable forecasting on energy-related carbon dioxide (CO 2 ) emissions is of great significance for climate policy decision making and energy planning. Due to the complicated nonlinear relationships of CO 2 emissions with its driving forces, the accurate forecasting for CO 2 emissions is a tedious work, which is an important issue worth studying. In this study, a novel CO 2 emissions prediction method is proposed which employs the latest nature-enlightened optimization method, named the Whale optimization algorithm (WOA), to search the optimized values of two parameters of LSSVM (least squares support vector machine), namely the WOA-LSSVM model. Meanwhile, the driving forces of CO 2 emissions including GDP (gross domestic product), energy consumption and population are chosen to be the import variables of the proposed WOA-LSSVM method. Taking China’s CO 2 emissions as an instance, the effectiveness of WOA-LSSVM-based CO 2 emissions forecasting is verified. The comparative analysis results indicate that the WOA-LSSVM model is significantly superior to other selected models, namely FOA (fruit fly optimization algorithm)-LSSVM, LSSVM, and OLS (ordinary least square) models in terms of CO 2 emissions forecasting. The proposed WOA-LSSVM model has the potential to effectively improve the accuracy of CO 2 emissions forecasting. Meanwhile, as a new nature-enlightened heuristic optimization algorithm, the WOA has the prospect for wide application.

Keywords: CO 2 emissions prediction; Whale optimization algorithm; LSSVM; parameters optimization; driving forces (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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