INTEGRATION OF MACHINE LEARNING FOR MICROCLIMATE MANAGEMENT OPTIMIZATION IN BUILDINGS: PERSPECTIVES AND OPPORTUNITIES

Abstract

Modern machine learning (ML) technologies offer significant opportunities for optimizing microclimate management systems in buildings. In this article, we explore the potential application of ML methods for forecasting, adaptive control, and optimization of heating, ventilation, and air conditioning (HVAC) systems in buildings. We examine ML methods used for analyzing weather data, working hours, thermal needs, and user preferences to automatically optimize HVAC parameters. Additionally, we discuss the application of ML for detecting faults and preventing failures in microclimate systems, contributing to increased reliability and efficiency of building operations. Finally, we consider prospects for personalizing comfortable microclimates in buildings based on user preferences. Our analysis identifies the potential of ML for creating sustainable, energy-efficient, and comfortable buildings that meet modern requirements for microclimate management.