Human-centric computing for inequality energy classification in smart cities: Who gets left to margins while improving quality of life through advanced approaches?

In the dynamic landscape of smart cities, effective energy classification stands as a critical determinant of societal well-being. This paper introduces a Hybrid SVM-Random Forest model designed for intricate energy classification tasks. A comprehensive evaluation, encompassing performance metrics, confusion matrices, feature importance analysis, and execution time comparisons, reveals its exceptional prowess. With superior accuracy, heightened interpretability, and remarkable efficiency, the Hybrid SVM-Random Forest emerges as a compelling solution for energy classification in smart cities. Beyond predictive accuracy, the model empowers stakeholders by unraveling valuable insights into intricate features shaping predictions. Its efficient execution not only sets a benchmark for practical application but aligns seamlessly with the goal of human -centric computing. Addressing energy inequality nuances, the model becomes a transformative force, contributing significantly to the smart city's quality of life enhancement. Exploring the Hybrid SVM-Random Forest model's multifaceted dimensions showcases its capacity to transcend boundaries, becoming a catalyst for societal transformation. With a nuanced understanding of energy consumption patterns, it becomes an agent of change, offering tangible solutions to complex challenges in energy classification and inequality. This research not only presents a state-of-the-art computational model but paves the way for a more inclusive and resilient urban future.