Residential load event detection in NILM using robust cepstrum smoothing based method

Abstract

Event detection has an important role in detecting the switching of the state of the appliance in the residential environment. This paper proposed a robust smoothing method for cepstrum estimation using double smoothing i.e. the cepstrum smoothing and local linear regression method. The main problem is to reduce the variance of the home appliance peak signal. In the first step, the cepstrum smoothing method removed the unnecessary quefrency by applying a rectangular window to the cepstrum of the current signal. In the next step, the local regression smoothing weighted data points to be smoothed using robust least squares regression. The result of this research shows the variance of the peak signal is decreased and has a good performance with better accuracy. In noise enviromment, performance prediction quite good with values greater than 0.6 and relatively stable at values above 0.9 on SNR> 25 for single appliances. Furthermore, in multiple appliances, performance prediction quite good at SNR> 20 and begins to decrease in SNR <20 and SNR> 25.

Machine learning algorithms for electrical appliances monitoring system using open-source systems

“…Due to the hardware and software complexity in the NALM application, Semwal and Prasa focused on optimization algorithms using minimum features from smart meters [18]. Iksan et al proposed a smoothing method for filtering out peak signals [19]. The system achieved better accuracy with this method.…”

Solving multiple linear regression problem using artificial neural network

“…The independent variables will be continuous or categorical (dummy coded as appropriate). MLR presented in (1) and Figure 1 can be solved using MATLAB Figure 2 [2]- [4].…”

Optimal Solution of Classification (Prediction) Problem

“…Based on the input of features or patterns [4][5][6]. The classifier can be used to distinguish the inputted data by performing needed actions to output a predicted class.…”