An Improved Load Feature Extraction Technique for Smart Homes Using Fuzzy-Based NILM

Abstract

With the advancement of smart residential buildings, houseowners are increasingly interested to monitor the connected electrical loads in view of energy conservation and load management. Smart grid technology in the distribution system includes cutting-edge load monitoring applications, permitting efficient use of electrical energy. Nonintrusive load monitoring (NILM) technique is such a tool to recognize the consumption of energy for individual electrical loads in a residential system at a solitary point of measurement. In this article, an improved NILM method is proposed using a shunt passive filter installed at the source end of residential building with the help of current harmonic signature analysis. A fuzzy rule-based intelligent identification method is employed in order to detect and monitor different electrical loads considering various load features at the utility side. The major contribution of the proposed technique is the improvement in identification accuracy of household loads with multiple appliances. The connected filter additionally restricts the injected harmonics generated by the loads at the source end. The usage of the fuzzy technique also reduces the processor memory requirement than conventional machine learning techniques. Simulations backed by experimental results validate the viability of the proposed methodology.