Multivariate consumption profiling (MCP) for intelligent meter systems: a methodology to define categories and levels

2010 ◽  
Vol 10 (5) ◽  
pp. 710-720 ◽  
Author(s):  
J. L. Solanas ◽  
M. R. Cussó
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Reference Population ◽  
Statistical Analyses ◽  
Conceptual Schema ◽  
Data Variability ◽  
Water Companies ◽  
Analyse Data

Multivariate Consumption Profiling (MCP) is a methodology to analyse the readings made by Intelligent Meter (IM) systems. Even in advanced water companies with well supported IM, full statistical analyses are not performed, since no efficient methods are available to deal with all the data items. Multivariate Analysis has been proposed as a convenient way to synthesise all IM information. MCP uses Factor Analysis, Cluster Analysis and Discriminant Analysis to analyse data variability by categories and levels, in a cyclical improvement process. MCP obtains a conceptual schema of a reference population on a set of classifying tables, one for each category. These tables are quantitative concepts to evaluate consumption, meter sizing, leakage and undermetering for populations and groupings and individual cases. They give structuring items to enhance “traditional” statistics. All the relevant data from each new meter reading can be matched to the classifying tables. A set of indexes is computed and thresholds are used to select those cases with the desired profiles. The paper gives an example of a MCP conceptual schema for five categories, three variables, and five levels, and obtains its classifying tables. It shows the use of case profiles to implement actions in accordance with the operative objectives.

scholarly journals Geochemical Anomaly Separation by Statistical and Fractal Methods in the Sehezar Valley of Tonekabon – Northern Iran

2019 ◽  
Author(s):  
Meysam Yazdani ◽  
Firouz Alinia
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Multivariate Analysis ◽  
Pearson Correlation ◽  
Univariate Analysis ◽  
Screening Method ◽  
Bivariate Analysis ◽  
Geochemical Anomaly ◽  
Threshold Method ◽  
Fractal Methods

Sehezar area is located in southern Tonokabon in Mazandaran province, north of Iran, near the Tarom – Hashdtjin belt. The existence of granitoid masses in the region can be important in terms of the potential of mineralization. Geochemical anomaly separation from the background is one of the important steps in mineral exploration. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods (like univariate, bivariate, multivariate statistics), spatial statistical methods and fractal and multi-fractal methods. To identify the anomalous area, 71 stream sediment samples were collected from the area and analyzed by the ICP-MS method, and then interpreted. Initially, data were normalized and afterwards, univariate analysis (threshold limit and screening (P.N) methods) was used, in which results of the probable and definite anomaly of the threshold method were confirmed by the P.N screening method. Finally, the maps of the anomal zones were drawn. Then, bivariate analysis (Pearson correlation coefficients) and multivariate analysis on normal data were performed on SPSS software, in which factor analysis and cluster analysis were used for multivariate analysis. As a result of using the factor analysis method, six factors were identified and factor maps were drawn by the Surfer software. Also, by using cluster analysis, the variables were divided into two groups. In order for a better separation of the geochemical anomaly from the background, in addition to the threshold method, the Concentration - Area fractal method was used. Here, the fractal geometry using full-logarithmic graphs of the Concentration - Area obtained is capable of separating the stairs of different sections (background, threshold, and anomaly) with respect to the angle coefficient of the Concentration - Area plot. Then, in conclusion, results of these methods were compared and investigated, and finally, the anomalies area maps of the Au, Ag, Cu, Fe, W elements were drawn by Concentration - Area fractal and threshold methods and anomalous zones were introduced.

Multivariate analysis for morphological traits of the Hamra goat population in two regions of Morocco

2016 ◽  
Vol 59 ◽  
pp. 55-62
Author(s):  
B. Hilal ◽  
S. El Otmani ◽  
M. Chentouf ◽  
I. Boujenane
Keyword(s):  
Factor Analysis ◽  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Variance Component ◽  
Morphological Traits ◽  
Head Width ◽  
Body Measurements ◽  
Morphometric Traits ◽  
Goat Population ◽  
Two Populations

SummaryThe goal of this study was to characterize the Hamra goat population and to determine if Hamra goats of Beni Arouss and Rommani regions belong to the same population. Eleven morphometric traits of 157 Hamra animals (94 from Beni Arouss and 63 from Rommani) were used for this study. Overall, heart girth, body length, height at withers (HaW), height at rump (HS), chest depth (ChD), pelvis width (PW), chest width (CW), cannon circumference, head length (HeL), head width (HeW) and horn length (HL) of Hamra goats averaged 81.3, 61.5, 64.8, 65.3, 40.9, 19.3, 20.2, 9.67, 28.0, 26.3 and 23.4 cm, respectively. The effect of region was significant only on HaW, PW, HeL, HeW and HL, indicating certain homogeneity among goats of the two regions. Moreover, the inter region variance component ranged from 0 percent (absence of variability) for HS, CW, ChD and ChD to 18.5 percent for HeL, suggesting that the variability of body measurements between Beni Arouss and Rommani regions is very low. The factor analysis revealed four factors, which accounted for 73.5 percent of the total variance. The most discriminant variables between the two populations were HeL, HeW, PW and CW. The Mahalanobis distance between the two populations was 1.197, suggesting that there was genetic exchange between the two populations. The discriminant analysis showed that 80.9 percent of Rommani and 50.0 percent of Beni Arouss individuals were classified into their respective population. Results obtained will help in developing improvement and preservation strategies for the Hamra goat population.

scholarly journals Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland

2020 ◽  
Vol 27 ◽  
pp. 80-89
Author(s):  
Tymoteusz I. Miller
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Anthropogenic Activities ◽  
Chemometric Methods ◽  
Quality Indices ◽  
Water Quality Indices ◽  
Chl A

Chemometric methods, such as cluster analysis, factor analysis and discriminant analysis were applied to identify and assess the quality of lake water. Samples were collected from the Rusałka Lake, located in Szczecin City from September 2012 to September 2015 with frequency once a month. 25 water quality indices were evaluated in particular: Chl a, Eh, temperature, pH, COD-Mn, COD-Cr, BOD5, DO, NO3-, NO2-, NH4+, TN, SRP, TP, Ca2+, Mg2+, Cl-, SO42- ,HCO3-, Fetot, Mntot, Pb, Zn, Cd, Cu. Cluster analysis was performed to determine the similarity in terms of variation of the examined water quality indices and to determine seasonal variation between inflow and outflow areas of the lake. Factor analysis revealed that water quality is shaped by high anthropogenic activities. Discriminant analysis was used for the final assessment of which of the studied variables discriminate between the inflow and outflow zones and seasons. The chemometric approach and results provided useful information on the type of parameters affecting the quality of water in the analyzed lake. The data and information obtained can lead to better understanding of changes which are present in small flow lakes under high anthropopressure.

scholarly journals Relationship of total bacterial and somatic cell counts with milk production and composition – multivariate analysis

2018 ◽  
Vol 41 (1) ◽  
pp. 42568 ◽  
Author(s):  
Fernanda Scolari Botton ◽  
Dileta Regina Moro Alessio ◽  
Marcos Busanello ◽  
Catia Letícia Corrêa Schneider ◽  
Fernanda Hammes Stroeher ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Somatic Cell ◽  
Milk Production ◽  
Inverse Relationship ◽  
Bacterial Count ◽  
Total Bacterial Count ◽  
Cell Counts ◽  
Production Composition

 This study was realized to evaluate the monthly production, composition and quality of milk (total and defatted dry extract, lactose, fat and protein) and their relation to somatic cell count (SCC) and total bacterial count (TBC) using multivariate statistical analyses. The data are from a dairy farm for the period of two years (from January 2015 to December 2016). The SCC and TBC variables were transformed to somatic cell score (SCS) and log10 (LogTBC). Factor analysis, discriminant analysis and cluster analysis were used. Through factor analysis, it was found two factors that together explained 69.5% of the total data variation. The first factor represented the inverse relationship between lactose versus fat and protein content, while the second factor represented the inverse relationship among monthly milk yield versus SCS and LogTBC. The discriminant analysis identified that lactose and protein contents and SCS were the variables that had the greatest participation in the separation of the groups formed by the cluster analysis. The groups differed mainly by the monthly production of milk, composition and SCS. Finally, there are important multivariate relations between the variables milk production, composition and quality.

scholarly journals Εφαρμογές πολυπαραμετρικών στατιστικών τεχνικών στη χημική ανάλυση

2012 ◽  
Author(s):  
Ελένη Φαρμάκη
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Classification Trees ◽  
Components Analysis ◽  
Artificial Neural

Σε αυτή τη διατριβή μελετήθηκε η εφαρμογή πολυπαραμετρικών τεχνικών σε μεγάλες βάσεις δεδομένων ταξινόμησης, με σκοπό τη θεωρητική τους παρουσίαση, τη σύγκριση αυτών και την εξαγωγή συμπερασμάτων, σχετικά με το πεδίο εφαρμογής τους και το χειρισμό τους, τις δυνατότητες και τους περιορισμούς τους. Χρησιμοποιήθηκαν μη επιβλεπόμενες τεχνικές όπως Principal Components Analysis/Factor Analysis (PCA/FA) και Cluster Analysis (CA) αλλά και επιβλεπόμενες όπως Discriminant Analysis (DA), Classification Trees (CT) και Artificial Neural Networks (ANN). Ιδιαίτερη έμφαση δόθηκε στις τεχνικές CT και ANN (μελετήθηκαν τρεις μέθοδοι και αρχιτεκτονικές αντίστοιχα για καθεμιά από αυτές). Ερευνήθηκαν τα πλεονεκτήματα, μειονεκτήματα και ιδιαιτερότητες τους και βελτιστοποιήθηκαν τα μοντέλα ταξινόμησης των τεχνικών. Όλες οι τεχνικές συγκρίθηκαν μεταξύ τους, με κριτήριο τα αποτελέσματα τους (της ορθής ταξινόμησης των δειγμάτων) σε τρεις βάσεις δεδομένων οι οποίες αφορούσαν τους προσδιορισμούς α) μετάλλων-μεταλλοειδών στους τρεις ταμιευτήρες που χρησιμοποιούνται για την ύδρευση της πρωτεύουσας (Υλίκη, Μόρνο και Μαραθώνα), β) μετάλλων-μεταλλοειδών και ανόργανων στοιχείων σε θαλάσσια δείγματα ιζημάτων από μεγάλες ιχθυοκαλλιέργειες της χώρας, γ) σπανίων γαιών σε δείγματα ελαιολάδων από διάφορες περιοχές. Η DA αν και είναι παραμετρική τεχνική με πολλούς περιορισμούς στην εφαρμογή της, ανταποκρίθηκε στις ανάγκες των προβλημάτων και παρείχε πάντα μια πρώτη άποψη για το πρόβλημα (δυνατότητα ή όχι γραμμικού διαχωρισμού των ομάδων με βάση το Canonical plot της ανάλυσης και αρχική αξιολόγηση των μεταβλητών). Τα ποσοστά ορθής ταξινόμησης που παρείχε ήταν αρκετές φορές συγκρίσιμα με των πιο προηγμένων τεχνικών. Τα CT με 3 διαφορετικές μεθόδους και αρκετή ευελιξία (παρείχαν πολλές παραμέτρους προς δοκιμή και βελτιστοποίηση), επέτυχαν υψηλά ποσοστά ταξινόμησης με λίγες ή πολλές μεταβλητές (περισσότερες συνήθως των ANN), κατασκευάζοντας επαναλήψιμα μοντέλα με δυνατότητες γενίκευσης. Τα ANN αποδείχθηκαν ιδιαίτερα ευέλικτη τεχνική, με δυνατότητες αποτελεσματικής αξιολόγησης των μεταβλητών και εφαρμογής τους σε απλές αλλά και πολυπλοκότερες βάσεις προσεγγίζοντας γραμμικές και μη γραμμικές συναρτήσεις. Κατασκευάστηκαν ανθεκτικά και ευέλικτα μοντέλα. Μειονέκτημά τους αποτέλεσαν ωστόσο, τα φαινόμενα υπερπροσαρμογής που παρουσιάζουν και χρειάστηκαν προσεκτικοί χειρισμοί για την αποφυγή τους. Έτσι, τα διαθέσιμα δείγματα διαχωρίστηκαν σε τρεις ομάδες: χρησιμοποιήθηκαν εκτός της συνήθους ομάδας εκπαίδευσης, επιπλέον ομάδες επικύρωσης και ελέγχου. Με τον τρόπο αυτό, έγινε άμεση ταυτοποίηση των φαινομένων υπερπροσαρμογής (ώστε να διακόπτεται αυτόματα η εκπαίδευση του μοντέλου), αλλά και δοκιμή των μοντέλων σε νέα, “’άγνωστα” δείγματα, ώστε να ελέγχεται η δυνατότητα γενίκευσης αυτών. Ο διαχωρισμός σε ομάδες έγινε είτε τυχαία (όπως επιτάσσει η σύγχρονη βιβλιογραφία), είτε με βάση της προκατεργασίας με DA (μέθοδος που δεν έχει χρησιμοποιηθεί ποτέ στο παρελθόν). Επιπλέον, έγινε προσπάθεια εφαρμογής όσο το δυνατόν απλούστερων δομών με λίγες παραμέτρους (μεταβλητές, βάρη) αλλά και λειτουργικές μονάδες επεξεργασίας (νευρώνες).

scholarly journals Statistical Analyses of Femur Parameters for Designing Anatomical Plates

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Lin Wang ◽  
Kunjin He ◽  
Zhengming Chen
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Individual Differences ◽  
Discriminant Analysis ◽  
Statistical Methods ◽  
Proper Class ◽  
The Mean ◽  
Parameter Values ◽  
Bayes Discriminant Analysis

Femur parameters are key prerequisites for scientifically designing anatomical plates. Meanwhile, individual differences in femurs present a challenge to design well-fitting anatomical plates. Therefore, to design anatomical plates more scientifically, analyses of femur parameters with statistical methods were performed in this study. The specific steps were as follows. First, taking eight anatomical femur parameters as variables, 100 femur samples were classified into three classes with factor analysis and Q-type cluster analysis. Second, based on the mean parameter values of the three classes of femurs, three sizes of average anatomical plates corresponding to the three classes of femurs were designed. Finally, based on Bayes discriminant analysis, a new femur could be assigned to the proper class. Thereafter, the average anatomical plate suitable for that new femur was selected from the three available sizes of plates. Experimental results showed that the classification of femurs was quite reasonable based on the anatomical aspects of the femurs. For instance, three sizes of condylar buttress plates were designed. Meanwhile, 20 new femurs are judged to which classes the femurs belong. Thereafter, suitable condylar buttress plates were determined and selected.

Adolescents’ Market Segmentation: Using CSI as a Tool

2017 ◽  
Vol 21 (4) ◽  
pp. 425-435
Author(s):  
Rashmi Singh ◽  
Janmejay Singh
Keyword(s):  
Decision Making ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Market Segmentation ◽  
Design Methodology ◽  
Indian Adolescents ◽  
Homogeneous Cluster ◽  
Shopping Orientation ◽  
Decision Making Styles

Purpose of this article: The study aims to examine the adolescents’ decision-making style and shopping orientation using consumer style inventory (CSI) as a segmentation tool. It proposes that adolescents’ decision-making styles are quite different from the adult consumers. Here, the researchers develop a typology that is based on the dimensions of CSI and its ability to predict the homogeneous cluster. Design/methodology/approach: The study is based on the clustering of adolescents on the basis of their decision-making styles. The study was carried out in India. A sample of 215 students has been taken. Factor analysis, cluster analysis which is followed by discriminant analysis, has been used in the study. Findings: The article provides a significant segmentation of adolescents’ market on the basis of their decision-making style. And at last in this article, we conclude that the adolescents have different decision-making styles than the adult consumers. Six out of eight dimensions of the Sproles and Kendall (1986) are relevant to the Indian adolescents. On the basis of these traits/styles, the adolescents are grouped into three different clusters, which are homogeneous and identifiable in nature. Originality/value: This article fulfils an identified need to study about the adolescents and how they have been clustered into different segments. The CSI is used as segmentation tool. This is the first study to segment the Indian adolescents market on the basis of their decision-making styles.

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