A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

Guoqiang Sun, Tong Chen, Zhinong Wei, Yonghui Sun, Haixiang Zang and Sheng Chen
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Guoqiang Sun: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Tong Chen: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Zhinong Wei: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Yonghui Sun: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Haixiang Zang: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Sheng Chen: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China

Energies, 2016, vol. 9, issue 1, 1-16

Abstract: Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on variational mode decomposition (VMD) and spiking neural networks (SNNs) is proposed. An original carbon price series is firstly decomposed into a series of relatively stable components through VMD to simplify the interference and coupling across characteristic information of different scales in the data. Then, a SNN forecasting model is built for each component, and the partial autocorrelation function (PACF) is used to determine the input variables for each SNN model. The final forecasting result for the original carbon price can be obtained by aggregating the forecasting results of all the components. Actual InterContinental Exchange (ICE) carbon price data is used for simulation, and comprehensive evaluation criteria are proposed for quantitative error evaluation. Simulation results and analysis suggest that the proposed VMD-SNN forecasting model outperforms conventional models in terms of forecasting accuracy and reliability.

Keywords: carbon price forecasting; variational mode decomposition (VMD); spiking neural network (SNN); partial autocorrelation function (PACF); comprehensive evaluation criteria (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

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