The Application of Machine Learning and Deep Learning Techniques for Global Energy Utilization Projection for Ecologically Responsible Energy Management

Accurately estimating future energy consumption is critical as the world seeks alternatives to fossil fuels amidst rising energy demands. The research employs various prediction models for global energy prediction with GDP analysis in energy consumption context. These models include Regression models that are Linear, Polynomial, Bayesian, Tree, Extreme Gradient Boosting, K Nearest Neighbour, Stacked Model, Random Forest (RF), also Long Short-Term Memory (LSTM) and Convolution Neural Networks (CNN) methods. Models are employed to enhance global energy consumption modelling, analysing their adaptability to varying weather and social conditions. A comparative investigation shows that RF performs better than other Regression models. LSTM models perform better than RF in predicting the primary energy consumption per capita and GDP growth, with the lowest MSE value of 0.002 with comparatively higher time and processing complexity. However, RF outperforms in predicting renewable energy share, access to clean cooking fuel, CO2 emission and GDP per capita analysis. The study's novelty lies in its comprehensive evaluation of machine learning and deep learning methods across multiple geographic and temporal energy consumption patterns, emphasizing the superiority of advanced techniques in accurately modelling global energy usage.