An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes

In the smart home environment, efficient energy management is a challenging task. Solutions are needed to achieve a high occupant comfort level with minimum energy consumption. User comfort is measured in terms of three fundamental parameters: (a) thermal comfort, (b) visual comfort and (c) air quality. Temperature, illumination and CO 2 sensors are used to collect indoor contextual information. In this paper, we have proposed an improved optimization function to achieve maximum user comfort in the building environment with minimum energy consumption. A comprehensive formulation is done for energy optimization with detailed analysis. The Kalman filter algorithm is used to remove noise in sensor readings by predicting actual parameter values. For optimization, we have used genetic algorithm (GA) and particle swarm optimization (PSO) algorithms and performed comparative analysis with a baseline scheme on real data collected for a one-month duration in our lab’s indoor environment. Experimental results show that the proposed optimization function has achieved a 27 . 32 % and a 31 . 42 % reduction in energy consumption with PSO and GA, respectively. The user comfort index was also improved by 10 % i.e., from 0 . 86 to 0 . 96 . GA-based optimization results were better than PSO, as it has achieved almost the same user comfort with 4 . 19 % reduced energy consumption. Results show that the proposed optimization function gives better results than the baseline scheme in terms of user comfort and the amount of consumed energy. The proposed system can help with collecting the data about user preferences and energy consumption for long-term analysis and better decision making in the future for efficient resource utilization and overall profit maximization.