A decentralized federated learning based interoperable and heterogeneity aware predictive optimization method for energy and comfort in smart homes environment

Abstract

It is a known fact that a comfortable residential space or workspace is crucial to productivity and well-being. In smart home environments, sensors can be used to sense environment and occupant data and actuators can be controlled accordingly for maximizing thermal comfort. However, keeping in view the growing demand of energy and scarcity of energy generation sources, it is mandatory to optimize the energy usage complementary to optimize thermal comfort. In order for the developed method to be interoperable and heterogeneity aware across different edge device vendors and for ease of hardware connectivity in smart homes, a REST API based framework is developed. Sensors mounted with IoT devices collect data from its respective smart home environment and store the data in its local database. An edge server is used to control the connectivity among these IoT devices and to initiate the decentralized federated learning mechanism. Each IoT device train local deep learning models for predicting energy usage, indoor temperature, indoor humidity for maintaining a comfortable environment state using minimal energy. Models from each IoT devices are aggregated at each IoT device iteratively to make federated learning independent of central server. Global deep learning model is iteratively trained for each prediction requirement. Later, these models are used to predict indoor temperature, humidity and energy consumption for time stamp (t+1), keeping in view environmental and occupant related sensed data at time stamp (t). Optimization of energy consumption and thermal comfort is mathematically modeled and based on the predictions of DL models, environment is controlled by handling actuators using a particle swarm optimization (PSO) mechanism. Relevant constraints are incorporated in PSO for limiting the positional variations in particles for efficient learning. The method continually learns and adapts to changes in the surroundings, providing real-time monitoring and control of thermal comfort to the end users. To validate our approach, data is collected and experiments are conducted in a real-time smart home’s environment. The results are compared with those obtained without using the proposed predictive optimization mechanism. The study demonstrates the effectiveness of proposed predictive optimization mechanism in providing a comfortable and productive smart home or workspace environment utilizing 38% less energy as compared to competitor.