OPTIMIZATION FOR INTELLIGENT BUILDING AUTOMATION SYSTEMS FOR ENERGY EFFICIENCY USING AI-ML

Intelligent building automation systems (IBAS) are becoming increasingly popular for managing energy usage and improving energy efficiency in buildings. However, the effectiveness of these systems is highly dependent on their optimization. In this research paper, we propose the use of optimization techniques, including machine learning and artificial intelligence, to improve the energy efficiency of IBAS. We present a case study in which we optimize an IBAS for a commercial building, demonstrating the effectiveness of optimization techniques in reducing energy usage and costs. Intelligent Building Automation Systems (IBAS) are an increasingly popular solution to optimize energy efficiency and reduce energy consumption in buildings. IBAS rely on a combination of hardware and software to monitor and control various building systems, such as heating, ventilation, air conditioning, lighting, and security, in an integrated and automated way. Optimization is a crucial aspect of IBAS, as it enables the system to operate at its maximum efficiency by minimizing energy waste while ensuring occupant comfort. Intelligent Building Automation Systems (IBAS) that utilize Artificial Intelligence (AI) and Machine Learning (ML) techniques can play a critical role in improving energy efficiency in buildings. This paper examines the application of AI-ML in IBAS to optimize energy consumption while maintaining comfort levels for occupants. AI-ML algorithms can analyse real-time data from various building systems and sensors to predict equipment maintenance requirements, adjust heating, cooling, and lighting based on occupancy patterns, and use weather data to optimize energy consumption. The paper highlights how IBAS using AI-ML can significantly reduce operational costs, enhance occupant comfort and reduce carbon footprint. Additionally, it discusses the challenges and opportunities in implementing IBAS using AI-ML. The study concludes that IBAS utilizing AI-ML techniques are effective in achieving energy efficiency, and their implementation can lead to significant energy savings and a sustainable future