Adaptive residential demand-side management using rule-based techniques in smart grid environments

Programmable Communicating Thermostats (PCTs) as price-responsive thermostats are being used broadly for automatic control of residential HVAC systems in North America. The main advantage of existing PCTs is their capability to receive pricing signals from smart meters and shed HVAC load during high demand. In these cases; PCT automatically reduces the initialized set points to the levels pre-adjusted by user. However, there still exist neglected potentials for demand-side management that resides in the control and interaction of PCTs. Hence, it requires making PCTs more intelligence to learn and adapt to user's preference changes which results in adaptive demand-side management. In this paper, an algorithm which is based on the integration of rule-based techniques and wireless sensors capabilities is presented. The proposed algorithm is embedded into exiting PCTs to improve their capabilities in learning and adapting to occupant's pattern changes. The simulation results demonstrate that PCTs equipped with our approach performs better than existing PCTs with respect to energy saving and adapting to occupant's pattern changes. To verify the feasibility of the algorithm; an embedded system using ZigBee wireless communication is built and applied to a conventional air conditioning (AC) system. The experimental results show the system precisely adapts to user's preference changes while saving energy and cost.