The use of machine learning (ML) in sustainable systems-An Overview
This study examines how different teams used their in-depth understanding of wearable technologies to accomplish their goals in this study and shows that machine learning may be used to increase energy efficiency and save a lot of money.
Abstract
Machine learning has the ability to dramatically improve sustainable systems by anticipating needs, maximizing resource use, raising output, and lowering waste. An overview of earlier studies on the incorporation of machine learning into sustainable systems is presented together with a case study of how machine learning was used to lower energy use in a residential structure. The results show that machine learning may be used to increase energy efficiency and save a lot of money. Wearable technology has added a whole new dimension to the already vast field of personal electronics. The mobile phone gave devices their true individuality. Because so many services are designed around mobile phones, the market has opened up for a brand-new personalized experience utilizing wearable technologies. Fabric sensors may now be combined with wearable microcontrollers like the flora and lily pad to monitor stretch, pressure, bend, and even the direction that the body is being braced. The connections between them are based on conductive threads that follow the curve of the fabric. We'll examine how different teams used their in-depth understanding of wearable technologies to accomplish their goals in this study.