A Hybrid Automata-Driven Machine Learning Framework for Real-Time Energy Optimization in Smart Buildings

As city populations continue to expand, the need for effective energy management in smart buildings becomes essential to sustainable urban growth. Conventional energy forecasting techniques are usually not responsive to dynamic energy environments and, therefore, create inefficiencies in energy distribution. This study presents a hybrid approach that combines Finite Automata (FA) with sophisticated Machine Learning (ML) algorithms and Artificial Intelligence (AI) to minimize energy consumption in smart buildings. The system takes advantage of real-time data from IoT-enabled sensors and external APIs to capture important building parameters like square footage, occupancy rate, ambient temperature, and past energy consumption. These inputs train the predictive models like Random Forest, XGBoost, LightGBM, and Neural Networks to predict energy requirements. The Finite Automata supply organized state transitions for managing device operation to provide context-aware, adaptive energy management. The intended hybrid solution effectively improves forecasting efficiency, enhances energy efficiency, and facilitates smart scheduling of energy-driven systems. Not only does the framework optimize the use of resources, but also it minimizes carbon footprint as well as costs of operation for a more efficient and sustainable smart city infrastructure.