Control of electrically heated floor for building load management: A simplified self-learning predictive control approach

This paper is aimed at developing a new simplified predictive controller to manage an electrically heated floor for shifting and/or shaving the building peak energy demand. The function of the developed controller is to increase the rate of energy storage during off-peak hours and to decrease it during peak periods, while maintaining occupants' thermal comfort. To achieve this goal without using a detailed building model, a simplified solar predictive model, using available online weather data has been proposed. The controller approach is based on a learning process; it takes building responses of previous days into consideration. The developed algorithm was applied on two models of a single-storey building, with and without basement. Results show a significant decrease in thermal discomfort, average applied powers during peak periods and mid-peak periods. The approach has also proven to be financially attractive to both supplier and consumer.