A Dual-spine Approach to Load Error Repair in a HEMS Sensor Network

Liu, Xiaodong; Liu, Qi; Sun, Mingxu

Abstract

In a home energy management system (HEMS), appliances are becoming diversified and intelligent, so that certain simple maintenance work can be completed by appliances themselves. During the measurement, collection and transmission of electricity load data in a HEMS sensor network, however, problems can be caused on the data due to faulty sensing processes and/or lost links, etc. In order to ensure the quality of retrieved load data, different solutions have been presented, but suffered from low recognition rates and high complexity. In this paper, a validation and repair method is presented to detect potential failures and errors in a domestic energy management system, which can then recover determined load errors and losses. A Kernel Extreme Learning Machine (K-ELM) based model has been employed with a Radial Basis Function (RBF) and optimised parameters for verification and recognition; whilst a Dual-spline method is presented to repair missing load data. According to the experiment results, the method outperforms the traditional B-spline and Cubic-spline methods and can effectively deal with unexpected data losses and errors under variant loss rates in a practical home environment.

Citation

Liu, X., Liu, Q., & Sun, M. (2018). A Dual-spine Approach to Load Error Repair in a HEMS Sensor Network. Computers, Materials & Continua, 57(2), 179-194. https://doi.org/10.32604/cmc.2018.04025

• Journal Article Type: Article
• Acceptance Date: Aug 16, 2018
• Online Publication Date: Dec 31, 2018
• Publication Date: Dec 31, 2018
• Deposit Date: Oct 5, 2018
• Publicly Available Date: Dec 31, 2018
• Journal: Computers, Materials & Continua
• Print ISSN: 1546-2218
• Electronic ISSN: 1546-2226
• Publisher: Tech Science Press
• Peer Reviewed: Peer Reviewed
• Volume: 57
• Issue: 2
• Pages: 179-194
• DOI: https://doi.org/10.32604/cmc.2018.04025
• Keywords: Electric load data analysis, home energy management, quality assurance and control.
• Public URL: http://researchrepository.napier.ac.uk/Output/1311623
• Contract Date: Oct 4, 2018