Machine learning techniques for IoT-driven Cognitive Sustainable Buildings
Modern intelligent IoT-based buildings place a growing emphasis on energy efficiency. In this field, Machine Learning (ML) can be a key component in enhancing energy efficiency. By using data from sensors or other technologies, ML can help to analyze patterns, forecast energy demand, and improve building controls, so increasing the efficiency of such buildings and creating the so-called cognitive sustainable smart buildings. This can take users to save a lot of energy and, consequently, money. During my Ph.D., I will apply ML techniques to IoT data from smart buildings with the aim of providing the community with new instruments for optimizing energy consumption in buildings.