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

2019 ◽  
Vol 9 (2) ◽  
pp. 742
Author(s):  
Nur Iksan ◽  
Jaka Sembiring ◽  
Nanang Hariyanto ◽  
Suhono Harso Supangkat
Keyword(s):  
Performance Prediction ◽  
Event Detection ◽  
Smoothing Method ◽  
Local Linear Regression ◽  
Linear Regression Method ◽  
Least Squares Regression ◽  
Local Regression ◽  
Rectangular Window ◽  
Double Smoothing ◽  
Robust Smoothing

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.

scholarly journals Penerapan Metode Double Exponential Smoothing dan Moving Average pada Peramalan Permintaan Produk Gasket Cap di PT. Nesinak Industries

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Muhammad Hafidh Kurniawan ◽  
Dene Herwanto
Keyword(s):  
Moving Average ◽  
Smoothing Method ◽  
Percentage Error ◽  
Product Demand ◽  
Demand Prediction ◽  
Automotive Components ◽  
Double Exponential ◽  
Application Data ◽  
Double Smoothing ◽  
Double Exponential Smoothing

PT. Nesinak Industries is a company which focuses on the manufacturing process of an electronic component as well as automotive components (vehicle). In business activities, such as production, a strategy is required to survive in competition. Planning and forecasting are a strategy that can be implemented to accomplish these goals. In this study, the data used are previous sealing application data from January 2019 to March 2021. The objective of this study is to forecast product demand over the next period in order to be able to respond to customer demand. Data processing in this study utilize the Brown exponential  double smoothing method  and the moving average is then determined with the lowest MAPE (Mean Absolute Percentage Error) value to be used for the company’s product demand prediction calculations. The value of taken from Brown's exponential dual smoothing method is the value of with the two lowest error values from 0.1 to 0.9, whose value with the least error value is = 0.8 and = 0.9. In terms of the moving average method, the researchers tested a period of three months and a period of four months. In the MAPE calculation, the results of exponential double smoothing = 0.8 of 26.92 %, exponential double smoothing = 0.9 of 26.22 %, moving average n = 3 of 32.46%, and moving average n = 4 of 34.77%.

Functional Availability Modelling of CNC Machine Tools

2014 ◽  
Vol 915-916 ◽  
pp. 395-399 ◽  
Author(s):  
Xiao Bing Li ◽  
Jun Gao ◽  
Zheng Zhang ◽  
Xiao Cui Zhu
Keyword(s):  
Least Square Method ◽  
Smoothing Method ◽  
Least Square ◽  
Cnc Machine Tools ◽  
Linear Regression Method ◽  
Cnc Machine ◽  
Basis System ◽  
Functional Linear Regression ◽  
Availability Model ◽  
Fitting Parameters

A new method for calculating the instantaneous availability was proposed based on Functional Data Analysis method. It introduced the Quadratic Bernstein Polynomial into the smoothing method firstly for the reliability which estimated by median rank method and estimated the fitting parameters by least square method. Then, under the assumption that the maintenance difficult of the CNCs was decreasing over the work time, chosen the appropriate smoothing basis function based on the trend after the time section adjustment for the estimated maintainability value. The Fourier basis system and the non-linear least squares were selected for the maintainability function smoothing method and the fitting parameters. Finally, the instantaneous availability model of CNCs was built based on the functional linear regression method, and a case example of 15 CNCs was given.

scholarly journals An open-access CMIP5 pattern library for temperature and precipitation: description and methodology

2017 ◽  
Vol 9 (1) ◽  
pp. 281-292 ◽  
Author(s):  
Cary Lynch ◽  
Corinne Hartin ◽  
Ben Bond-Lamberty ◽  
Ben Kravitz
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Regression Method ◽  
Pattern Generation ◽  
Cmip5 Models ◽  
Climate Projections ◽  
Linear Regression Method ◽  
High Latitudes ◽  
Least Squares Regression ◽  
Temperature And Precipitation

Abstract. Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squares regression methods. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60–90° N/S). Bias and mean errors between modeled and pattern-predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within  ≤  0.5 °C, but the choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. This paper describes our library of least squares regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns. The dataset and netCDF data generation code are available at doi:10.5281/zenodo.495632.

scholarly journals Sector Based Linear Regression, a New Robust Method for the Multiple Linear Regression

2018 ◽  
Vol 23 (4) ◽  
pp. 1017-1038 ◽  
Author(s):  
Gábor Nagy
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Robust Regression ◽  
Dimensional Space ◽  
Simple Algorithm ◽  
Regression Method ◽  
Linear Regression Method ◽  
Least Squares Regression ◽  
Cloud Processing ◽  
Point Cloud Processing

This paper describes a new robust multiple linear regression method, which based on the segmentation of the N dimensional space to N+1 sector. An N dimensional regression plane is located so that the half (or other) part of the points are under this plane in each sector. This article also presents a simple algorithm to calculate the parameters of this regression plane. This algorithm is scalable well by the dimension and the count of the points, and capable to calculation with other (not 0.5) quantiles. This paper also contains some studies about the described method, which analyze the result with different datasets and compares to the linear least squares regression. Sector Based Linear Regression (SBLR) is the multidimensional generalization of the mathematical background of a point cloud processing algorithm called Fitting Disc method, which has been already used in practice to process LiDAR data. A robust regression method can be used also in many other fields.

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