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

Abstract

Abstract:The accurate forecasting of carbon dioxide (CO 2 ) 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 CO 2 emission of the World top-5 CO 2 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.

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

“…In other studies, various methods are utilized differently, and their applications vary in context. Chang, Sun and Gu [13] presented a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model to forecast energy CO 2 emissions. This study's finding was able to certify the validity of the presented approach, while it also revealed that the forecasting precision can be enhanced to a certain degree.…”

Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

“…The concept of quantum computing is presented by Benioff [49] and Feynman [50] in the 1980s through combing quantum theory and information science. In quantum computing, the qubit is adopted to express the information; and the theory of superposition, entanglement and collapse of states is employed for information computation [39,40,42,51]. Quantum computing greatly improves the computational efficiency and has attracted widespread interest and research.…”

“…In classical HSA, the algorithm chooses an upper neighboring value with 50% probability and lower with 50% around new vector. Inspired by Fibonacci theorem, 0.618 is chosen as the border of vector adjusting direction, shown in Equation (12) [51,53].…”

A COMBINATION FORECAST FOR ENERGY-RELATEDCO2EMISSIONS IN THE UNITED STATES

“…The wide ranging models that have been adopted range from state space and dynamic computable general equilibrium models to the use of quantum harmony search algorithms. Whilst this paper is not interested in presenting an extensive literature coverage, those interested are directed to read some recent articles such as [5][6][7][8][9][10][11][12][13]. Noteworthy is that none of the research (both recent and historical) is able to produce a single forecasting model that outperforms the rest, as each evaluated model appears to have its own advantages and disadvantages.…”

“…Noteworthy is that none of the research (both recent and historical) is able to produce a single forecasting model that outperforms the rest, as each evaluated model appears to have its own advantages and disadvantages. Furthermore, there has been emphasis on providing combination forecasts for CO 2 emissions [13]. In this paper, the SSA technique is introduced as an addition to the already wide ranging list of models for forecasting CO 2 emissions.…”