A clustering-based short-term load forecasting using independent component analysis and multi-scale decomposition transform

2022 ◽  
Author(s):  
Roohollah Keshvari ◽  
Maryam Imani ◽  
Mohsen Parsa Moghaddam
Keyword(s):  
Independent Component Analysis ◽  
Load Forecasting ◽  
Component Analysis ◽  
Independent Component ◽  
Short Term ◽  
Multi Scale ◽  
Short Term Load Forecasting

scholarly journals Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis

2021 ◽  
Author(s):  
K Ramakrishna Kini ◽  
Muddu Madakyaru
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environment ◽  
Process Data ◽  
Multi Scale ◽  
Efficient Information ◽  
Non Gaussian

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

scholarly journals Knowledge-guided multi-scale independent component analysis for biomarker identification

2008 ◽  
Vol 9 (1) ◽  
pp. 416 ◽  
Author(s):  
Li Chen ◽  
Jianhua Xuan ◽  
Chen Wang ◽  
Ie-Ming Shih ◽  
Yue Wang ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Biomarker Identification ◽  
Multi Scale

scholarly journals Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 88058-88071 ◽  
Author(s):  
Zhuofu Deng ◽  
Binbin Wang ◽  
Yanlu Xu ◽  
Tengteng Xu ◽  
Chenxu Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Multi Scale ◽  
Short Term Load Forecasting

scholarly journals MSICA: multi-scale signal decomposition based on independent component analysis with application to denoising and reliable multi-channel

2020 ◽  
Vol 1 (1) ◽  
pp. 25-35
Author(s):  
Abolfazl Hajisami ◽  
Dario Pompili
Keyword(s):  
Independent Component Analysis ◽  
Multiple Scales ◽  
Digital Signal ◽  
Component Analysis ◽  
Scale Space ◽  
Independent Component ◽  
Signal Denoising ◽  
Multi Scale ◽  
Information Preservation ◽  
Bank Structure

Multi-scale decomposition is a signal description method in which the signal is decomposed into multiple scales, which has been shown to be a valuable method in information preservation. Much focus on multi-scale decomposition has been based on scale-space theory and wavelet transform. In this article, a new powerful method to perform multi-scale decomposition exploiting Independent Component Analysis (ICA), called MSICA, is proposed to translate an original signal into multiple statistically independent scales. It is proven that extracting the independent components of the even and odd samples of a digital signal results in the decomposition of the same into approximation and detail. It is also proven that the whitening procedure in ICA is equivalent to a filter bank structure. Performance results of MSICA in signal denoising are presented; also, the statistical independency of the approximation and detail is exploited to propose a novel signal-denoising strategy for multi-channel noisy transmissions aimed at improving communication reliability by exploiting channel diversity.

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