A HUMAN BEHAVIOR RECOGNITION SYSTEM FOR SMART ENERGY AND RESOURCE OPTIMIZATION USING DEEP LEARNING TO ENCOURAGE SUSTAINABLE HABITS IN SMART CITIES

Abstract

Smart technology development has emerged to manage urban resource consumption because sustainable solutions are necessary for urban environments. The majority of present-day systems that control power and water supply focus solely on resource management without considering how human actions impact resource use. The proposed research develops a full computer system that employs deep learning models to detect human behavior patterns in real-time while generating personalized suggestions to decrease waste production. A system gathers multiple sensor modes and visual data to handle different behavioral activities which includes monitoring spaces being occupied and users operating appliances and levels of water usage. The evaluation employs deep learning techniques to analyze these behaviors for extracting patterns regarding resource usage. The system provides efficient suggestions about turning off extra devices and adjusting water consumption which develops knowledge through continuous user feedback. The system differs from traditional both energy and water systems because it integrates behavior identification into resource management functions to enable adaptive feedback which adapts to environmental situations. Through this approach sustainability gains development and users gain the capability to adopt environmentally friendly behaviors. The proposed research presents a scaleable system design that will advance the development of sustainable future-oriented urban dwelling spaces.