Abstract
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.
Highlights
With the advent of industrialization and globalization, World energy consumption has increased exponentially by about 30% in the last 25 years [1]
Assigning the same β value for all separate models and forecasting period in all application cases is somewhat unreasonable since it affects the proportion of each individual model forecasting results in the combination model forecasting results
The optimization algorithm provides a valid way to solve these problems through optimizing objective function (MAPE in this work) to find the optimal β values
Summary
With the advent of industrialization and globalization, World energy consumption has increased exponentially by about 30% in the last 25 years [1]. It is vital to select the β value in order to achieve an optimal combination result with minimum error The purpose of this investigation is to develop an effective way to search for the optimal β values for each single model in the combination model by using a quantum harmony search (QHS) algorithm and to establish the QHS algorithm-based optimization DMSFE combination forecasting method. Apart from the QHS algorithm-based DMSFE combination model, other cases with different given β values (β = 0.1, 0.5 and 1 respectively) are designed to compare with the proposed model to test the performance through forecasting error indicators. The forecasting results and scenario analysis of applying the same optimal β value to all individual models of DMSFE combination forecasting model basing on QHS algorithm are given for the same purpose.
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