Simulation and Energy Management in Smart Environment Using Ensemble of GA and PSO

Abstract

Smart, intelligent and sustainable power consumption model in residential sector received attraction of the researchers in last couple of years. Numerous techniques have been implemented for green and smart power management but the problem of minimum power consumption without compromising user comfort in green buildings is a big challenge to the researchers. In the past, we have presented power consumption optimization models for green buildings which are constructed on principles of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Up-to some extent, the previous intelligent models accomplished better output results, but the results shows that there is space to improve the results furthermore. In this paper, we propose an advanced energy optimization and energy control model based on multiprocessing, ensemble of PSO and GA named Advanced Energy Optimization (AEO) to provide better occupants comfort index and efficient power utilization of the energy sources. The focus of the proposed AEO model is to maximize occupant’s indoor comfort and minimize power consumption. The paper also emphases on the application of a simulator to control the actuators and update the indoor environment. The proposed AEO intelligent building model delivers power efficient green environment by minimizing power utilization and enhancing occupant’s comfort as opposed to GA based power consumption model (GAP). The proposed AEO model also provides better comfort index as compared to GAP, Single Optimization with Hybrid Prediction (SOHP), PSO and Ant Bee Colony with Knowledge Base (ABCKB) models. The results shows the usefulness of the proposed AEO model in reducing consumed power and improving the user’s comfort as compared to existing models. The model also control the building actuators based on the control information’s provided by the model.