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

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.

Automated summary

Energy is crucial for the development of a country, and its consumption, production, and transition to green energy are essential for sustainable development. Forecasting technologies, especially grey systems, are gaining attention due to their ability to analyze a limited amount of data. In this study, an optimized grey machine learning model using a polynomial structure was used to predict power generation, consumption, and CO2 emissions, outperforming conventional grey models in terms of accuracy.