Occupant comfort management is an important feature of a smart home, which requires achieving a high occupant comfort level as well as minimum energy consumption. Based on a large amount of data, learning models enable us to predict factors of a mathematical model for deriving the optimal result without expensive experiments. Comfort management supports high-level comfort to the occupant in the individual indoor environment, using the optimal power consumption to run home appliances. In this paper, we propose occupant comfort management based on energy optimization, using an environment prediction model. The proposed energy optimization model provides optimal power consumption based on the proposed objective function, which requires temperature and comfort index data as the input parameters. For the input requirement, temperature prediction model and humidity prediction model are presented based on a recurrent neural network with a pre-collected dataset, including indoor and outdoor temperature and humidity sensing data. Using the predicted temperature and humidity data, the comfort index model derives the predicted mean vote value to be used in the energy optimization model with the predicted temperature data. The experimental results present an 8.43% reduction of the optimized power consumption compared to the actual power consumption using mean absolute percentage error to calculate. Moreover, the emulation of an indoor environment using optimal energy consumption presents as approximately similar to the actual data.
A New Prediction Model for Power Consumption with Local Weather Information
