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Open AccessArticle

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

by Hong Chang 1, Wei Sun 2 and Xingsheng Gu 1,*
1
Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
2
School of Economics and Management, North China Electric Power University, Baoding, Hebei 071003, China
*
Author to whom correspondence should be addressed.
Energies 2013, 6(3), 1456-1477; https://doi.org/10.3390/en6031456
Received: 7 January 2013 / Revised: 14 February 2013 / Accepted: 28 February 2013 / Published: 6 March 2013
he accurate forecasting of carbon dioxide (CO2) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β) arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn’t consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE) objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO2 emission of the World top‒5 CO2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE) and mean absolute error (MAE) are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree. View Full-Text
Keywords: fossil fuel energy; CO2 emissions; quantum harmony search algorithm; discounting factor; combination forecasting method fossil fuel energy; CO2 emissions; quantum harmony search algorithm; discounting factor; combination forecasting method
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Chang, H.; Sun, W.; Gu, X. Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model. Energies 2013, 6, 1456-1477.

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