Universiti Teknologi Malaysia Institutional Repository

Short-term CO2 emissions forecasting using multi-variable grey model and Artificial Bee Colony (ABC) algorithm approach

Shabri, Ani and Samsudin, Ruhaidah and Hezzam, Essa Abdullah (2022) Short-term CO2 emissions forecasting using multi-variable grey model and Artificial Bee Colony (ABC) algorithm approach. In: The 5th International Conference of Reliable Information and Communication Technology 2020, 21 - 22 December 2021, Langkawi, Malaysia.

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Official URL: http://dx.doi.org/10.1007/978-3-030-70713-2_54

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Artificial Bee Colony, Carbon dioxide emissions forecasting, Multi-variable grey model
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:100279
Deposited By: Widya Wahid
Deposited On:29 Mar 2023 07:20
Last Modified:29 Mar 2023 07:20

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