An Energy-efficient Knowledge-based Smart Fan System Using Analytic Hierarchy Process and Fuzzy Logic

Electric fans have a big contribution to the electricity consumption of families in most of the highly populated regions of the world with hot and humid climates. Furthermore, with increasing the number of middle-income families, comfortability is becoming a significant factor in choosing electric fans. A smart fan can reduce electricity bills due to its high efficiency and create a personal comfort zone for the users due to its intelligent processing system. This paper proposes an automatic knowledge-based smart fan system that uses the Analytic Hierarchy Process (AHP) and a fuzzy interference system for performing the intelligent operations. The system is equipped with appropriate sensors to acquire ambient temperature, gas, and light data. The AHP component and fuzzy inference system determine the speed of the fan efficiently and intelligently. The simulation results verify that the energy consumption of the smart system is nearly 35% less than that of the non-smart system. In addition, comparisons with some other existing fan systems show the energy efficiency of the proposed fan system. The prototype of the proposed system is built to observe the simulation performance and effectiveness via experiments.