Machine Learning-Based Energy Optimization in Smart Buildings and Industrial Facilities

Abstract

The growing energy demand, acceleration of urbanization and expansion of industrial operations have raised the demand for intelligent energy management strategies for achieving energy efficiency and cost savings while minimizing carbon emissions. Most traditional energy management systems are based on rule based control structures and statistical methods that are not able to adjust to dynamic occupancy patterns, varying environmental conditions, and complex industrial processes. In recent years, a new technique, namely Machine Learning (ML), has emerged as a viable solution to predict, optimize, and automatically control energy use in smart buildings and industrial systems. The paper examines the various ML techniques for energy optimization in detail, and categorizes them into five areas: energy optimization for HVAC systems, energy optimization for lighting control, integration of renewable energy systems, energy optimization for industrial processes, and predictive maintenance. The proposed framework involves the combination of IoT sensors, real-time data collection, data preprocessing, feature engineering, development of ML models, and optimization algorithms, all aimed at realizing intelligent energy management. A set of supervised, unsupervised, deep learning, and reinforcement learning algorithms is examined, such as Random Forest, Support Vector Machine, XGBoost, Artificial Neural Network, Long Short-Term Memory network and Deep Reinforcement Learning with regard to their prediction accuracy, computational efficiency and energy saving requirement. As shown in the comparative analysis, advanced ML models consistently outperform traditional methods in predicting energy consumption, detecting consumption trends and reducing equipment idle time, as well as optimizing operational schedules. The results show that an energy optimization approach based on ML can greatly contribute to energy efficiency, operational cost savings, comfort of the occupants, and sustainable production in industry. The paper also presents the challenges and opportunities that are currently being faced, such as data quality, model interpretability, scalability, cybersecurity, and real-time deployment, and suggests future research directions, including the application of explainable AI, edge computing, digital twins, federated learning and autonomous energy management systems. In this study, the researchers give an overview of emerging ML techniques and practical lessons to the researchers and practitioners to develop intelligent, scalable and sustainable solutions for energy optimization in next-generation smart buildings and industrial facilities.