Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm

Shenghua Xiong,Dan Ma,Chunfeng Wang,Zhenming Fang

doi:10.3390/en12010147

Abstract

The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon emissions in China. However, it is challenging to improve the carbon price forecasting accuracy due to its non-linearity and non-stationary characteristics, especially in multi-step-ahead forecasting. In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) is proposed. VMD is employed to extract the primary mode for the carbon price. Then, FMRVR, which is used as the forecasting module, is built on the preprocessed data. To achieve high accuracy and stability, the MOWOA is utilized to optimize the kernel parameter and input the lag of the FMRVR. The proposed hybrid forecasting model is applied to carbon price series from three major regional carbon emission exchanges in China. Results show that the proposed VMD-FMRVR-MOWOA model achieves better performance compared to several other multi-output models in terms of forecasting accuracy and stability. The proposed model can be a potential and effective technique for multi-step-ahead carbon price forecasting in China’s three major regional emission exchanges.

Highlights

Global climate change induced by greenhouse emissions has become a serious challenge to the sustainable development of society, which has attracted worldwide attention recently
To compensate for the above gaps and promote the accuracy in multi-step-ahead carbon price forecasting, we propose a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) for carbon price series from three major carbon emission exchanges in China
Two-step, and three-step ahead forecasting. These results show that the VMD-FMRVR has (c) The proposed model outperforms the MOTP-MOWOA and MOGP-MOWOA in one-step, twoa stronger performance in Shenzhen emission allowance 2016 (SZA2016) price forecasting thanthat thethe step, and three-step ahead forecasting

Summary

Introduction

Global climate change induced by greenhouse emissions has become a serious challenge to the sustainable development of society, which has attracted worldwide attention recently. An emission trading scheme as an effective market mechanism to reduce carbon emission is widely applied worldwide [1]. In response to the challenge of global warming, China has established eight pilot regional emission exchanges in Shenzhen, Guangdong, Hubei, Tianjin, Shanghai, Chongqing, Beijing, and Fujian since. Accurate carbon price forecasting is critical for governors to make policies [3] and important for the reasonable production arrangement of carbon emission companies to reduce costs and increase profit. Promoting the accuracy of carbon price forecasting is a challenge for academics and an important topic in the field of energy policy and energy consumption

Methods

Results

Conclusion