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

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.

Data Completion for Power Load Analysis Considering the Low-rank Property

With large-scale applications,the loss of power load data during transmission is inevitable.This paper proposes a data completion method considering the low rank property of the data.According to the low-rank property of data and numerical experiments,we find either the linear interpolation(LI)or the singular value decomposition(SVD)based method is superior to other methods depending on the smoothness of the data.We construct an index to measure the smoothness of data,and propose the SVDLI algorithm which adaptively selects different algorithms for data completion according to the index.Numerical simulations show that irrespective of the smoothness of data,the data complementing results of SVDLI are comparable to or better than the best of SVD or LI algorithms.The present study is verified using the measurements in China,and the public data of the Australian electricity distribution company and Lawrence Berkeley National Laboratory.