Abstract
Carbon dioxide (CO2) emissions is one of the recent global issues where the negative influence and effect on the environment is high. Enhancing the degree of awareness among public and concerned authorities and developing forecasting methods and techniques form a vital solution to this issue. The aim of this research is to enhance the forecasting efficiency of the traditional GM(1,N) model by proposing and modifying background values of GM(1,N) using a new algorithms. This paper presents the Artificial Bee Colony (ABC) to select the optimal weight of background values for a traditional GM(1,N) model. The data of CO2 emissions, GDP per capita, the amount invested in Malaysia, population, total energy consumption and number of registered motor vehicles during the period from 2000 to 2016 is used to verify the applicability and effectiveness of the model. The numerical example results indicate that the new model is performing well compared to the traditional GM(1,N) model.
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Shabri, A., Samsudin, R., Hezzam, E.A. (2021). Short-Term CO2 Emissions Forecasting Using Multi-variable Grey Model and Artificial Bee Colony (ABC) Algorithm Approach. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_54
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DOI: https://doi.org/10.1007/978-3-030-70713-2_54
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