scholarly journals Energy Disaggregation Using Principal Component Analysis Representation

2020 ◽  
Author(s):  
Pierre V. Dantas ◽  
Waldir Sabino S. Júnior ◽  
Celso B. Carvalho
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Smart Grids ◽  
Care Home ◽  
Principal Component ◽  
Audio Signals ◽  
Ubiquitous Systems ◽  
Energy Disaggregation ◽  
The Individual ◽  
Health Care Home

The main purpose of disaggregation is to decompose a signal into a set of other signals that together constitute it. This approach could be applied to audio signals, health care, home automation, ubiquitous systems and energy systems. It may be unworkable to individually measure the energy consumption of loads in a system simultaneously and, through disaggregation, we can make an inference using a main meter. The main contribution of this work is to use PCA to extract representativeness of an energy consumption signal we want to disaggregate, identifying its most relevant characteristics. The field of study is relevant because it allows information to be obtained in a simpler and cheaper way about the individual consumption of loads that make up a system. This opens up perspectives for other approaches such as smart grids and IoT. We demonstrate that when compared to other techniques, the proposal produces more accurate disaggregation results.

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

Development of a New Tool for Managing Performance Nutrition: The Athlete Food Choice Questionnaire

2019 ◽  
Vol 29 (6) ◽  
pp. 620-627 ◽  
Author(s):  
Rachael L. Thurecht ◽  
Fiona E. Pelly
Keyword(s):  
Principal Component Analysis ◽  
Nutrition Education ◽  
Food Choice ◽  
Weight Control ◽  
Principal Component ◽  
Component Analysis ◽  
Key Factors ◽  
Dietary Interventions ◽  
Frequency Scale ◽  
The Individual

This study aimed to develop and refine an Athlete Food Choice Questionnaire (AFCQ) to determine the key factors influencing food choice in an international cohort of athletes. A questionnaire that contained 84 items on a 5-point frequency scale was developed for this study. Athletes at the 2017 Universiade, in Taiwan, were invited to participate. Principal component analysis was utilized to identify key factors and to refine the questionnaire. Completed questionnaires were received from 156 athletes from 31 countries and 17 sports. The principal component analysis extracted 36 items organized into nine factors explaining 68.0% of variation. The nine factors were as follows: nutritional attributes of the food, emotional influences, food and health awareness, influence of others, usual eating practices, weight control, food values and beliefs, sensory appeal, and performance. The overall Kaiser–Meyer–Olkin measure was 0.75, the Bartlett test of sphericity was statistically significant, χ2(666) =2,536.50, p < .001, and all of the communalities remained >0.5. Intercorrelations were detected between performance and both nutritional attributes of the food and weight control. The price of food, convenience, and situational influences did not form part of the factorial structure. This research resulted in an AFCQ that includes factors specific to athletic performance and the sporting environment. The AFCQ will enable researchers and sports dietitians to better tailor nutrition education and dietary interventions to suit the individual or team. The next phase will test the accuracy and reliability of the AFCQ both during and outside of competition. The AFCQ is a useful tool to assist with management of performance nutrition for athletes.

Joint trajectory generator for powered orthosis based on gait modelling using PCA and FFT

Robotica ◽  
2017 ◽  
Vol 36 (3) ◽  
pp. 395-407 ◽  
Author(s):  
Nicholas B. Melo ◽  
Carlos E. T. Dórea ◽  
Pablo J. Alsina ◽  
Márcio V. Araújo
Keyword(s):  
Principal Component Analysis ◽  
Fourier Series ◽  
Energy Consumption ◽  
Lower Limb ◽  
Joint Angle ◽  
Principal Component ◽  
Orthotic Devices ◽  
Joint Angles ◽  
Anthropometric Features ◽  
Trajectory Generator

SUMMARYIn this work, we propose a method able to find user-oriented gait trajectories that can be used in powered lower limb orthosis applications. Most research related to active orthotic devices focuses on solving hardware issues. However, the problem of generating a set of joint trajectories that are user-oriented still persists. The proposed method uses principal component analysis to extract shared features from a gait dataset, taking into consideration gait-related variables such as joint angle information and the user's anthropometric features, used directly in an orthosis application. The trajectories of joint angles used by the model are represented by a given number of harmonics according to their respective Fourier series analyses. This representation allows better performance of the model, whose capability to generate gait information is validated through experiments using a real active orthotic device, analysing both joint motor energy consumption and user metabolic effort.

A strategy for selecting diverse accessions using Principal Component Analysis from a large germplasm collection of soybean

2003 ◽  
Vol 1 (2-3) ◽  
pp. 151-156 ◽  
Author(s):  
R. L Sapra ◽  
S. K. Lal
Keyword(s):  
Principal Component Analysis ◽  
Sample Size ◽  
Principle Component Analysis ◽  
Quantitative Traits ◽  
Germplasm Collection ◽  
Principal Component ◽  
Target Population ◽  
Component Analysis ◽  
Principle Component ◽  
The Individual

AbstractWe suggest a diversity-dependent strategy, based on Principle Component Analysis, for selecting distinct accessions/parents for breeding from a soybean germplasm collection comprising of 463 lines, characterized and evaluated for 10 qualitative and eight quantitative traits. A sample size of six accessions included all the three states, namely low, medium and high of the individual quantitative traits, while a sample of 16–19 accessions included all the 60–64 distinct states of qualitative as well as quantitative traits. Under certain assumptions, the paper also develops an expression for estimating the size of a target population for capturing maximum variability in a sample three accessions.

scholarly journals MEMONITOR KUALITAS PEMBELAJARAN DI FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNIVERSITAS UDAYANA

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
RAHMAD RAHMAD WIDODO ◽  
I PUTU EKA NILA KENCANA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Principal Component Analysis ◽  
Control Chart ◽  
Principal Component ◽  
Component Analysis ◽  
Natural Sciences ◽  
Quality Of Learning ◽  
Study Programs ◽  
The Individual ◽  
New Variables

Controlling the quality of learning is very important and influences the accreditation of study programs at the Faculty of Mathematics and Natural Sciences Udayana University, as a guarantor of the quality of graduates. Apply pricipal component analysis to reduce the number of determinant attributes of learning quality, with the aim of looking at the data structure with fewer variables. The control chart is a multivariate control chart that is used to view the potrait of the quality of learning in the Mathematics and Natural Sciences Faculty, using new variables obtained from principal component analysis. The results obtained from principal component analysis show that the contribution of the learning quality indicators is univen. The potrait of the quality of learning at the Faculty of Mathematics and Natural Sciences obtained from the individual-moving range (I-MR) and the control chart shows the need for corrective actions and monitor regularly to improve the quality of learning.

scholarly journals Dissecting individual differences in responses to naturalistic stimuli in functional MRI: effects of development and gender

2020 ◽  
Author(s):  
Xin Di ◽  
Bharat B. Biswal
Keyword(s):  
Principal Component Analysis ◽  
Individual Differences ◽  
Functional Mri ◽  
Principal Component ◽  
Component Analysis ◽  
Gender Effects ◽  
Delayed Response ◽  
List Type ◽  
And Gender ◽  
The Individual

AbstractFunctional MRI (fMRI) study of naturalistic conditions, e.g. movie watching, usually focuses on shared responses across subjects. However, individual differences in the responses have been attracting increasing attention in search of group differences or associations with behavioral outcomes. The individual differences have been studied by directly modeling the cross-subject correlation matrix or projecting the relations into a 1-D space. We contend that it is critical to examine whether there are single or multiple consistent components of responses underlying the whole population, because multiple components may undermine the individual relations using the previous methods. We use principal component analysis (PCA) to examine the heterogeneity of brain responses across subjects in terms of the eigenvalues of the covariance matrix, and utilize this approach to study developmental trajectories and gender effects in a movie watching dataset. We identified several brain networks in the parietal cortex that showed a significant second principal component (PC) of regional responses, which were mainly represented the younger children. The second PCs in some networks, i.e. the supramarginal network, resembled a delayed version of the first PCs for 4 seconds (2 TR), indicating delayed responses in the younger children than the older children and adults. However, no apparent gender effects were found in the first and second PCs. The analyses highlight the importance of identifying multiple consistent responses underlying individual differences in responses to naturalistic stimuli. And the PCA-based approach could be complementary to the commonly used intersubject correlation analysis.HighlightsThere may be multiple consistent responses among subjects during movie watchingPrincipal component analysis can be used to identify the multiple consistent responsesMany brain regions showed two principal components that were separated by ageYounger children showed delayed response in the supramarginal gyrus and precuneus

Quantification of Upper-Body Synergies: A Case Comparison for Stroke and Non-Stroke Victims

2016 ◽  
Author(s):  
Omid Heidari ◽  
John O. Roylance ◽  
Alba Perez-Gracia ◽  
Eydie Kendall
Keyword(s):  
Principal Component Analysis ◽  
Degrees Of Freedom ◽  
Principal Component ◽  
Component Analysis ◽  
Joint Space ◽  
Upper Body ◽  
Dimensional Synthesis ◽  
Revolute Joints ◽  
Case Comparison ◽  
The Individual

Motion synergies are principal components of the movement, obtained as combinations of joint degrees of freedom, that account for common postures of the human body. These synergies are usually obtained by capturing the motion of the human joints and reducing the dimensionality of the joint space with techniques such as principal component analysis. In this work, an experimental procedure to investigate the synergies of the upper body is developed and the results of the pilot study are shown. The upper-limb kinematics includes the joint complexes of the hand, wrist, forearm, elbow, and shoulder. The different kinematic models in the literature have been reviewed, and a serial chain is considered from the upper arm. A three degree of freedom (3-DOF) linkage containing two revolute joints and one prismatic joint has been chosen to simulate the shoulder motion. A spherical joint represents the Glenohumeral (GH) joint; the elbow and ulna-radius rotations are represented by two revolute joints and the wrist is modeled with two revolute joints. The hand has a tree structure and branches into the individual phalanges, with a 2-dof MCP joint and single R joints for the rest of the phalangeal joints. The data are collected using motion capture and the joint angles are calculated using a combination of dimensional synthesis and inverse kinematics. Principal component analysis can be used to extract the synergies for a set of previously-selected motions. The motions are performed by healthy subjects and subjects who have suffered stroke, in order to see the changes in the motion primitives. It is expected that this study will help quantify and classify some of the loss of motion due to stroke.

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