Design and implementation of a rule-based learning algorithm using Zigbee wireless sensors for energy management

The capabilities of wireless sensors networks (WSNs) to measure different variables, could significantly improve the limitations of the existing energy management systems. In this paper, we introduce a combination of rule-based techniques and wireless sensors to demonstrate the capabilities of wireless sensors in reducing the electricity consumption without sacrificing thermal comfort that would help utilities in peak load curtailments. The method is applied to existing programmable thermostats (PTs) to add more intelligence to this device for better energy management in residential buildings. The simulation results demonstrate that the proposed rule-based wireless thermostat performs better than the PTs in various aspects, i.e., learning, electric energy conservation, and occupant comfort that could help utilities in peak load curtailment. Moreover, our method is implemented on a typical residential Air Conditioner (AC) by using of X-bee wireless sensor and Arduino Microcontroller. Conducted results show that the combination of WSNs capabilities and the rule-based method reduce the energy consumption by 33.5% compared to the similar existing AC system.