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

In cold climates, the electrical power demand for space conditioning during certain periods of the day becomes a critical issue for utility companies from an environmental and financial point of view. Shifting a portion or all the demand to off-peak periods can help in reducing the peak demand and stress on the electrical grid. To predict the required energy that needs to be stored, predictive supervisory control strategies such as Model Predictive Control (MPC) have been developed, by which the future operating modes of storage systems can be preplanned. However, control strategies like MPC requires a building model and an optimization algorithm. Their development is time-consuming and also requires high implementation cost. 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. [ABSTRACT FROM AUTHOR]