IoT Task Management Mechanism Based on Predictive Optimization for Efficient Energy Consumption in Smart Residential Buildings

Energy-saving is a global challenge and one of the hot research topics of this decade. The need for sustainable technologies and solutions for energy-saving dramatically increased in residential buildings due to population growth, quality of indoor environment, and climate change. Recently, IoT based applications have been developed in smart homes, smart cities, smart hospitals, and other smart environments. The goals of sustainable technologies in residential buildings incorporate maximization of thermal comfort and minimizing energy consumption. The challenges and problems of residential buildings can be solved using consumer behavior models and integrating their inference into residential problem solutions. This paper proposes an IoT task management mechanism based on predictive optimization for energy consumption minimization in smart residential buildings. The proposed task management mechanism has a predictive optimization module based on prediction and an optimization module for solving energy consumption minimization problems. The energy data is obtained from different appliances to evaluate the proposed predictive optimization approach. The proposed approach results are compared with prediction and optimization modules. The performance is evaluated in terms of regression performance metrics. The case study results show that the predictive optimization mechanism based on task management performs better than standalone prediction and optimization-based energy consumption mechanisms in residential buildings.