Multi-Agent Architecture for Control of Heating and Cooling in a Residential Space

Abstract

Energy demand in a smart grid is directly related to energy consumption, as defined by user needs and comfort experience. This article presents a multi-agent architecture for smart control of space heating and cooling processes, in an attempt to enable flexible ways of monitoring and adjusting energy supply and demand. In this proposed system, control agents are implemented in order to perform temperature set-point delegation for heating and cooling systems in a building, offering a means to observe and learn from both the environment and the occupant. Operation of the proposed algorithms is compared with traditional algorithms utilized for room heating, using a simulated model of a residential building and real data about user behaviour. The results show (i) the performance of machine learning for the occupancy forecasting problem and for the problem of calculating the time to heat or cool a room; and (ii) the performance of the control algorithms, with respect to energy consumption and occupant comfort. The proposed control agents make it possible to significantly improve an occupant comfort with a relatively small increase in energy consumption, compared with simple control strategies that always maintain predefined temperatures. The findings enable the smart grid to anticipate the energy needs of the building.