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Development of Grey Machine Learning Models for Forecasting of Energy Consumption, Carbon Emission and Energy Generation for the Sustainable Development of Society

Author

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  • Akash Saxena

    (Department of Electrical Engineering, Central University of Haryana, Mahendergarh 123031, India)

  • Ramadan A. Zeineldin

    (Deanship of Scientific Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Ali Wagdy Mohamed

    (Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
    Department of Mathematics and Actuarial Science School of Sciences Engineering, The American University, New Cairo 11835, Egypt)

Abstract

Energy is an important denominator for evaluating the development of any country. Energy consumption, energy production and steps towards obtaining green energy are important factors for sustainable development. With the advent of forecasting technologies, these factors can be accessed earlier, and the planning path for sustainable development can be chalked out. Forecasting technologies pertaining to grey systems are in the spotlight due to the fact that they do not require many data points. In this work, an optimized model with grey machine learning architecture of a polynomial realization was employed to predict power generation, power consumption and CO 2 emissions. A nonlinear kernel was taken and optimized with a recently published algorithm, the augmented crow search algorithm (ACSA), for prediction. It was found that as compared to conventional grey models, the proposed framework yields better results in terms of accuracy.

Suggested Citation

  • Akash Saxena & Ramadan A. Zeineldin & Ali Wagdy Mohamed, 2023. "Development of Grey Machine Learning Models for Forecasting of Energy Consumption, Carbon Emission and Energy Generation for the Sustainable Development of Society," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1505-:d:1102089
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    References listed on IDEAS

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    1. Xiong, Xin & Hu, Xi & Guo, Huan, 2021. "A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption," Energy, Elsevier, vol. 234(C).
    2. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    3. Ping Jiang & Qingping Zhou & Haiyan Jiang & Yao Dong, 2014. "An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-13, June.
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    1. Ana Lorena Jiménez-Preciado & Salvador Cruz-Aké & Francisco Venegas-Martínez, 2024. "Identification of Patterns in CO 2 Emissions among 208 Countries: K-Means Clustering Combined with PCA and Non-Linear t -SNE Visualization," Mathematics, MDPI, vol. 12(16), pages 1-18, August.

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