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

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 CO2 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.

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

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“…Stationary energy 42 [58], [60], [55], [62], [69], [70], [27], [110], [29], [111], [73], [32], [77], [79], [81], [4], [39], [83], [41], [56], [88], [3], [89], [91], [45], [46], [94], [95], [97], [49], [57], [52], [100], [53], [54], [102], [103], [92], [104], [105], [106], [6] Transportation 10…”

“…The application of ML in architecture aims to enhance energy efficiency and reduce carbon footprints across various domains, including buildings, transportation, and industrial processes [70], [60]. In the context of buildings, architects can leverage ML algorithms to predict energy consumption patterns, identify potential savings, and optimize energy usage based on historical data and current conditions [60].…”

Prediction of agricultural carbon emissions in China based on a GA-ELM model

“…The third is about the trend prediction of agricultural carbon emissions. Scholars mainly rely on traditional forecasting methods to predict the peak value of agricultural carbon emissions, such as the environmental Kuznets curve (Pandey and Mishra, 2021;Ojaghlou et al, 2023), IPAT identity (Du et al, 2012;Yang et al, 2023), support vector machine model (Gao et al, 2022;, low-emission analysis platform (Sun et al, 2022b;Chen et al, 2023), gray forecasting model (Wang et al, 2023a;Saxena et al, 2023), and various combination models. On the basis of the extreme learning machine model (ELM), Wang et al (2020b) used the whale algorithm to optimize it and used the WOA-ELM model to predict China's carbon emissions, and the prediction results were more accurate.…”