A personalized home energy management system for residential demand response

Demand Response (DR) and Time-of-Use (TOU) pricing refer to programs offering incentives to customers who reduce their energy use during times of peak demand. A demand response system coordinates the demand and supply between the energy provider and the customer. In this study, a turn-key solution is developed to offer demand response services for residential customers. A cognitive residential ANFIS-based Master Energy Controller (MEC) is developed to forecast future power demand based on user's life style and environmental/social factors affecting power consumption. The Master Energy Controller implements a branch and bound scheduling algorithm for managing appliance runtime schedules. The MEC is developed to provide users with functionality to schedule appliances at home and automate demand response strategies without the burden of learning demand response strategies. The MEC monitors and controls appliances through intelligent appliance controllers connected by a self-organizing zero configuration network - also developed in this study. This paper discusses the system architecture for this energy management system and shows that such a system is a viable platform for demand response.