Enhanced power control model based on hybrid prediction and preprocessing/post-processing.

Since last decade, energy management and conservation in residential buildings received a great attraction of the researchers. A number of methods exist in the literature for energy conservation, but the trade-off between occupant comfort level and energy consumption is still a major challenge and needs more attention. Particle swarm optimization (PSO) and genetic algorithm (GA) based power control methodologies have been proposed previously. These techniques achieved good performance up-to some extent, but still there is room for improvements. In this paper, an enhanced optimized power control and hybrid prediction model based on preprocessing/post-processing, GA and hybrid prediction algorithms for occupants comfort index, energy saving and energy consumption prediction is proposed. Main focus is given to increase user's comfort index and minimize energy consumption using GA based optimized and hybrid predicted systems with preprocessing and post-processing of data. Proposed method provides energy efficient environment by reducing energy consumption and improving occupants comfort index as compared to previous GA based power prediction model. The proposed system is also compared with individual Kalman filter ARIMA model prediction. The comparative results show the efficiency of the proposed model in decreasing the predicted power consumption and enhancing the occupants comfort index. [ABSTRACT FROM AUTHOR]