Muhammad Kamran
University of Engineering and Technology, Pakistan
Title: Efficient nonintrusive load monitoring technique
Biography
Biography: Muhammad Kamran
Abstract
With the advancement in smart grids, easy access to load monitoring and management is becoming essential feature of this modern system. In order to comply with this parameter, a novel nonintrusive load monitoring (NILM) technique is proposed in this paper. This technique is found cost effective, state of the art load monitoring and energy disaggregation that can be employed in conjunction with smart grids for smart energy management systems. NILM involves extracting load signatures of individual appliances from composite voltage and current signals and identifying those signatures by employing machine learning techniques. An introduction of novel feature extraction techniques involving fractal analysis has made it novel and comparison is made with other methods involving discrete wavelet analysis. Research has highlighted that fractal analysis has become emerging method for data extraction and analysis even utilizing artificial intelligence and neural networks. The results obtained in this research are appreciable to verify the effectiveness of novel technique of data extraction using fractal analysis which saves the time and cost of data management system and has become valuable feature of smart grids.