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Energies 2016, 9(1), 54; doi:10.3390/en9010054

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

College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
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Authors to whom correspondence should be addressed.
Academic Editor: Wei-Chiang Hong
Received: 18 November 2015 / Revised: 6 January 2016 / Accepted: 11 January 2016 / Published: 19 January 2016
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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. View Full-Text
Keywords: carbon price forecasting; variational mode decomposition (VMD); spiking neural network (SNN); partial autocorrelation function (PACF); comprehensive evaluation criteria carbon price forecasting; variational mode decomposition (VMD); spiking neural network (SNN); partial autocorrelation function (PACF); comprehensive evaluation criteria
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sun, G.; Chen, T.; Wei, Z.; Sun, Y.; Zang, H.; Chen, S. A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks. Energies 2016, 9, 54.

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