Advanced Ensemble and Neural Fusion Techniques for Sustainable Energy Carbon Reduction Prediction

Abstract

The rapid growth of electricity consumption and industrial development has significantly increased carbon emissions, raising global concerns about environmental sustainability. Over the years, energy monitoring systems, smart grids, and digital data collection technologies have enabled organizations to gather large volumes of electricity-related data from power plants, distribution networks, and environmental monitoring systems. Analyzing this data has become essential for understanding patterns related to carbon emission reduction and improving energy management strategies. Traditionally, electricity consumption analysis relied on manual methods and basic statistical techniques where analysts used spreadsheets and historical reports to interpret energy usage patterns. However, these traditional approaches were time-consuming and unable to effectively process large and complex datasets generated by modern energy systems. They often failed to capture complex relationships between electricity consumption, environmental conditions, and emission reduction levels, leading to inefficient analysis and limited insights. Therefore, there is a need for advanced analytical techniques capable of handling large-scale electricity datasets and accurately identifying emission reduction patterns. In this study, machine learning techniques are applied to analyze electricity consumption data and classify carbon emission reduction categories using models such as Logistic Regression Classifier (LRC), Xtreme Gradient Boosting Classifier (XGBC), and Extra Trees Classifier (ETC). In addition, a hybrid analytical approach called the Neuro-Tree Fusion (NTF) model is utilized, which integrates a Graph Polynomial Neural Network (GPNN) for deep feature extraction with a Deep Neural Decision Tree (DNDT) tree-based classifier for final prediction. The integration of these computational models enables efficient processing of complex datasets and improves classification accuracy. This analytical approach supports better interpretation of electricity consumption patterns and contributes to data-driven environmental monitoring and sustainable energy management.