Abstract
Under smart power environment, mass electro-data bring new opportunities and challenges for user's electrical behavior analysis. This paper starts from individual user's electrical consumption monitoring to establish multi-dimensional load classification system and decompose individual user's electricity load. Then, for user's electrical behavior characteristics and user's electrical load characteristics, we apply improved K-means cluster to classify and analyze user's electrical behavior. On this basis, this paper designs a model for non-intrusive electrical load decomposition and recognition study to mine user's modes, assist grid company to formulate request correspondence and achieve the purpose of energy-saving and emission reduction.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
