文献类型：期刊文献

英文题名：Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

作者：Chang, Hong[1];Sun, Wei[2];Gu, Xingsheng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]North China Elect Power Univ, Sch Econ & Management, Baoding 071003, Hebei, Peoples R China

年份：2013

卷号：6

期号：3

起止页码：1456

外文期刊名：ENERGIES

收录：;EI(收录号：20132016341117);WOS:【SCI-EXPANDED(收录号:WOS:000316604500016)】；

基金：This work was supported by "the Fundamental Research Funds for the Central Universities (12MS137)".

语种：英文

外文关键词：fossil fuel energy; CO2 emissions; quantum harmony search algorithm; discounting factor; combination forecasting method

摘要：The 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 (beta) arbitrarily since beta 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 beta 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 beta 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.