Ranking of energy consumption objects using the principal components method

Ensuring comfortable conditions in civil buildings requires the implementation of tasks of monitoring and forecasting the cost of energy resources, as well as energy-efficient management of heating engineering systems and its equipment. The implementation of appropriate automation and monitoring solutions allows the accumulation of a significant amount of data. To increase the informativeness of the analysis of energy efficiency in the operation of civil buildings a model of their information ranking was developed using correlation analysis and the principal component analysis. Based on the interdisciplinary methodology of data analysis (CRISP-DM), the basic indicators were determined for the accepted initial conditions on electricity and heat consumption of the university buildings and the matrix of correlation coefficients of their interrelation was estimated. Certain data (external volume and area of the building and average temperature values for this region according to the norm) are obtained from the technical documentation of buildings and available from open sources, others (amount of consumed heat and electricity, indoor temperature) are determined during operation and characterize the efficiency of energy resources in the building. At the initial stage, a correlation analysis of the relationship between the main parameters that characterize buildings and their consumption of energy resources. The principal component analysis was used to reduce the dimensionality of the feature set of data and to identify homogeneous groups of energy consumption objects. The obtained four components explain about 90% of the variance of the initial data and characterize the efficiency of energy use in terms of temperature, volume and coefficient of heating degree days of the heating season. The obtained results are recommended for implementation in modern systems of energy monitoring and municipal energy management as applied models for diagnosing abnormal situations and sound management decisions. Keywords – buildings; energy consumption; principal components; machine learning; data segmentation.

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Estimation of Diversity of Living Standards in Polish Voivodships in 2003–2013 Using Principal Components for Multidimensional Functional Data and Cluster Analysis

Przegląd Statystyczny ◽

10.5604/01.3001.0014.1152 ◽

2016 ◽

Vol 63 (1) ◽

pp. 81-97

Author(s):

Mirosław Krzyśko ◽

Agnieszka Majka ◽

Waldemar Wołyński

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Principal Components ◽

Functional Data ◽

Principal Component ◽

Component Analysis ◽

Living Standards ◽

Homogeneous Groups ◽

And Cluster Analysis

The paper presents an estimation of life standard diversity for residents of Polish voivodships in 2003–2013. The principal component analysis was applied for multidimensional functional data and the dendrite method was used for cluster analysis. These methods made it possible to isolate relatively homogeneous groups of voivodships that had similar values of characteristics under consideration, for the whole period at issue.

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Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

Water Practice & Technology ◽

10.2166/wpt.2006.017 ◽

2006 ◽

Vol 1 (1) ◽

Author(s):

K. Katayama ◽

K. Kimijima ◽

O. Yamanaka ◽

A. Nagaiwa ◽

Y. Ono

Keyword(s):

Principal Component Analysis ◽

Prediction Model ◽

Principal Components ◽

Prediction Method ◽

Principal Component ◽

Component Analysis ◽

Rainfall Data ◽

Radar Rainfall ◽

Input Variables ◽

Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

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

Energies ◽

10.3390/en12010196 ◽

2019 ◽

Vol 12 (1) ◽

pp. 196 ◽

Cited By ~ 3

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.

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The Design of Index about Corporate Governance Based on PCA Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.4085 ◽

2014 ◽

Vol 926-930 ◽

pp. 4085-4088

Author(s):

Chuan Jun Li

Keyword(s):

Principal Component Analysis ◽

Corporate Governance ◽

Principal Components ◽

Principal Component ◽

Component Analysis ◽

Contribution Rate ◽

Variance Contribution ◽

Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

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Identification of Liquor Brands Based on near Infrared Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.834-836.935 ◽

2013 ◽

Vol 834-836 ◽

pp. 935-938

Author(s):

Lian Shun Zhang ◽

Chao Guo ◽

Bao Quan Wang

Keyword(s):

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Principal Components ◽

Near Infrared ◽

Scatter Correction ◽

Principal Component ◽

Component Analysis ◽

Correction Method ◽

Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

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Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2015000800003 ◽

2015 ◽

Vol 50 (8) ◽

pp. 649-657 ◽

Cited By ~ 2

Author(s):

Regina Maria Villas Bôas de Campos Leite ◽

Maria Cristina Neves de Oliveira

Keyword(s):

Principal Component Analysis ◽

Disease Severity ◽

Principal Components ◽

Leaf Spot ◽

Oil Content ◽

Principal Component ◽

Component Analysis ◽

High Yield ◽

Gge Biplot ◽

Alternaria Leaf Spot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

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Principal component analysis for evaluating a ranking method used in the performance testing in sheep of Morada Nova breed

Semina Ciências Agrárias ◽

10.5433/1679-0359.2015v36n6p3909 ◽

2015 ◽

Vol 36 (6) ◽

pp. 3909

Author(s):

Michelle Santos da Silva ◽

Luciana Shiotsuki ◽

Raimundo Nonato Braga Lôbo ◽

Olivardo Facó

Keyword(s):

Principal Component Analysis ◽

Principal Components ◽

Correlation Coefficients ◽

Performance Testing ◽

Principal Component ◽

Component Analysis ◽

Ranking Method ◽

Daily Weight Gain ◽

Meat Production ◽

Body Depth

A multivariate approach was adopted to evaluate the relationship among traits measured in the performance testing of Morada Nova sheep, verify the efficiency of a ranking method used in these tests and identify the most significant traits for use in future analyses. Data from 150 young rams participating in five versions of the performance tests for the Morada Nova breed were used. Twenty traits were measured in each animal: initial weight (IW), final weight (FW), average daily weight gain (ADG), loin eye area (LEA), scrotal circumference (SC), fat thickness (FT), conformation (C), precocity (Pc), muscularity (M), breed features (BF), legs (L), withers height (WH), chest width (CW), rump height (RH), rump width (RW), rump length (RL), body length (BL), body depth (BD), heart girth (HG) and body condition scoring (BCS). The Pearson’s correlation coefficients ranged from –0.10 to 0.93, with the highest correlations were between body weight variables and morphometric measurements. The three first principal components explained 72.28% of the total variability among all traits. The variables related to animal size defined the first principal component, whereas those related to visual appraisal and suitability for meat production defined the second and third principal components, respectively. The combination of traits from the principal component analysis showed that the ranking method currently used in the performance testing of Morada Nova sheep is efficient for selecting larger rams with better breed features and higher degrees of specialization for meat production.

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Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

10.5772/intechopen.101860 ◽

2022 ◽

Author(s):

Jaime González Maiz Jiménez ◽

Adán Reyes Santiago

Keyword(s):

Principal Component Analysis ◽

Stock Market ◽

Principal Components Analysis ◽

Principal Components ◽

Systematic Risk ◽

Principal Component ◽

Component Analysis ◽

Market Capitalization ◽

The Us ◽

Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

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Competitive and Recreational Running Kinematics Examined Using Principal Components Analysis

Healthcare ◽

10.3390/healthcare9101321 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1321

Author(s):

Wenjing Quan ◽

Huiyu Zhou ◽

Datao Xu ◽

Shudong Li ◽

Julien S. Baker ◽

...

Keyword(s):

Principal Component Analysis ◽

Principal Components ◽

Sagittal Plane ◽

Three Dimensional ◽

Frontal Plane ◽

Principal Component ◽

Component Analysis ◽

Ankle Inversion ◽

Motion System ◽

Recreational Runners

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners’ kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.

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Multiple Process Capability Improvement Method based on Principal Component Analysis

Mechanical Engineering Science ◽

10.33142/me.v1i1.654 ◽

2019 ◽

Vol 1 (1) ◽

Author(s):

Guangqi Ying ◽

Yan Ran ◽

Genbao Zhang ◽

Yuxin Liu ◽

Shengyong Zhang

Keyword(s):

Principal Component Analysis ◽

Principal Components ◽

Evaluation Method ◽

Process Capability ◽

Principal Component ◽

Component Analysis ◽

Multiple Process ◽

Improvement Method ◽

Capability Evaluation ◽

Capability Improvement

For the traditional multi-process capability construction method based on principal component analysis, the process variables are mainly considered, but not the process capability, which leads to the deviation of the contribution rate of principal component. In response to the question, this paper first clarifies the problem from two aspects: theoretical analysis and example proof. Secondly, aiming at the rationality of principal components degree, an evaluation method for pre-processing data before constructing MPCI using PCA is proposed. The pre-processing of data is mainly to standardize the specification interval of quality characteristics making the principal components degree more reasonable and optimizes the process capability evaluation method. Finally, the effectiveness and feasibility of the method are proved by an application example.

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