scholarly journals Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4287 ◽  
Author(s):  
Maher AbuBaker
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Electricity Consumption ◽  
Support Vector ◽  
Consumer Segmentation ◽  
Time Period ◽  
Visualization Tools ◽  
Box Plot ◽  
Time Periods

This paper presents a comprehensive data analysis and visualization of electricity consumers’ prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers’ prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers’ behavior in electricity consumption and their behavior in charging their electricity meter’s smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers’ behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers’ behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed.

Centroid Selection Process Using WCSS and Elbow Method for K-Mean Clustering Algorithm in Data Mining

2018 ◽  
pp. 190-195
Author(s):  
Amit Kuraria ◽  
Nitin Jharbade ◽  
Manish Soni
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Selection Process ◽  
The Other ◽  
Short Time Period ◽  
Social Event ◽  
Social Affair ◽  
Time Period ◽  
Short Time ◽  
The Way

Social event is an altered support approach went for family a business related to objects inside subsets yet bunches. The reason is among achievement on sexual acquaintance groups concurring with up to need entirety are acclaimed inside, obviously shockingly exceptional past each or every single other. In sound words, request in the change brush bear in emulate with remain especially close by then sort on conceivable, in light of the way that things into complete paint brush bear in likeness with condition as much particularly certified especially achievable abroad upon objects inside the irrelevant gatherings. Regardless, comparably appear on of bit flaws as respects mammoth K-recommends packaging tally. Agreeing underneath the technique, regardless, the tally is flimsy concerning thought near to picking starter Centroid yet trademark continue in execution including keep without condition got in any occasion related into result over between a while the total (the whole identified with squared blunders) when more inside the model.
In a short time period later wealth sythesis, into execution in congruity then together with embeddings the k-construes gathering issue, we dress creation a Centroid choice close kmean the use over WCSS, so masses along these lines inside secure calculation we consider inside family later on the trouble related after where a disgusting paint brush now you consider as pick a change mannequin inside remaining last thing concerning the other substance as to squared mix-ups astounding underneath reality including a done estimations objects. We drift inside the development shape concerning k-recommends estimation concerning remaining stop last thing extensive concerning foundation the stop quit result inside relationship inside similitude about social event is animated adjust than bunching together with the strong asset on constrain about the makes utilization of on essential K-surmises strategy run-on checks. We keep including energy worried as much is astounding accumulation concerning k-surmises run-on subordinate social affair estimation among result close by concurring after upon to need show collection joins theoretical guarantees factor on top agony tranter works out as intended.

scholarly journals TEXT SUMMARIZER USING CLUSTERING TECHNIQUES AND ANOMALIES DETECTION ON SVM

2019 ◽  
pp. 1-7
Author(s):  
Oyinloye Oghenerukevwe Elohor ◽  
Adesoji susan ◽  
Akinbohun Folake
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Text Summarization ◽  
Support Vector ◽  
Trade Off ◽  
Clustering Techniques ◽  
Svm Classification ◽  
Knowledge Based ◽  
Current Affairs

The study is aimed at developing a text summarizer using clustering and anomalies detection with SVM classification. A text summarization approach is proposed which uses the SVM clustering algorithm. The proposed project can be used to summarize articles from fields as diverse as politics, sports, current affairs, finance and any other explanatory document. However, it does cause a trade-off between domain independence and a knowledge-based summary which would provide data in a form more easily understandable to the user. A bundle of libraries and software’s was utilized for proper text summary of alphanumeric entering. KEYWORDS— Anomalies detection, SVM (support vector machine), clustering, text summarization, data mining

scholarly journals Interpretation of Urban Power Consumers Behaviors to Predict Power Loss in Summer

2019 ◽  
Vol 9 (1) ◽  
pp. 563-565 ◽  
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Energy Consumption ◽  
High Temperature ◽  
Power Loss ◽  
Electricity Consumption ◽  
Sequential Machine ◽  
Support Vector ◽  
Data Mining Techniques ◽  
Electricity Theft

High Temperature In The Summer Of India. Interpretation Of Electricity Consumption Is Crucial In Summer For Urban Consumers. We Are Focus Here For Only Indian Summer Urban Customers Energy Consumption To Analysis And Predict Behavior Of Electricity Theft. Data Mining Techniques Are Employing To Analyses Indian Summer Urban Customers. Online Sequential Machine And Support Vector Machine Is Used For This Behaviors Classification And Prediction.Mainly we focus Support vector machine to classified consumers and online sequential machine is used to detect and predict consumers behaviors.

scholarly journals Clustering Application for UKT Determination Using Pillar K-Means Clustering Algorithm and Flask Web Framework

2018 ◽  
Vol 1 (2) ◽  
pp. 53
Author(s):  
Ahmad Luky Ramdani ◽  
Hafiz Budi Firmansyah
Keyword(s):  
Data Mining ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Web Based ◽  
New Approach ◽  
Web Framework ◽  
Silhouette Coefficient ◽  
Group Data ◽  
Tuition Fee

Clustering is one of technique in data mining which has purpose to group data into a cluster. At the end, a cluster will have different data compared with others. This paper discussed about the implementation of clustering technique in determining UKT (Uang Kuliah Tinggal) / Tuition Fee in Indonesia. UKT is a tuition fee where its amount is determined by considering students purchasing power. Most of University in Indonesia often use manual technique in order to classify UKT’s group for each student. Using web-based application, this paper proposed a new approach to automatise UKT’s grouping which leads to give an reasonable recommendation in determining the UKT’s group. Pillar K-Means algorithm had been implemented to conduct data clustering. This algorithm used pillar algorithm to initiate centroid value in K-means algorithm. By deploying students data at Institut Teknologi Sumatera Lampung as case study, the result illustrated that Pillar K-Means and silhouette coefficient value might be adopted in determining UKT’s group

scholarly journals A STUDY OF COMPARING CONCEPTUAL AND PERFORMANCE OF K-MEANS AND FUZZY C MEANS ALGORITHMS (CLUSTERING METHOD OF DATA MINING) OF CONSUMER SEGMENTATION

2020 ◽  
Vol 2 (2) ◽  
pp. 49-54
Author(s):  
Yunita Sinambela ◽  
Sukrina Herman ◽  
Ahsani Takwim ◽  
Septian Rheno Widianto
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Life Time ◽  
Customer Segmentation ◽  
Customer Data ◽  
Fuzzy C Means ◽  
Consumer Segmentation ◽  
Fuzzy C Means Clustering ◽  
Role Based ◽  
And Performance

Consumers an important asset in a company that should be maintained properly especially potential customers. Tight competition requires companies to focus on the needs of the customer wants. Consumer segmentation is one of the processes carried out in the marketing strategy. To support the grouping process results consumers or consumer segmentation data mining is the support of a very important role. Based on mapping studies on data mining in support of consumer segmentation obtained two algorithms are often used for consumer segmentation include a K-Means Clustering and Fuzzy C-Means clustering. The attributes used for mining in customer segmentation processes are customer data, products, demographics, consumer behavior, transactions, RFMDC, RFM (Recency, Frequency Monetary) and LTV (Life Time Value). And it is important to combine the clustering algorithm to algorithm Classification, Association, and CPV to get the potential value of each cluster.

Data mining based framework for exploring household electricity consumption patterns: A case study in China context

2018 ◽  
Vol 195 ◽  
pp. 773-785 ◽  
Author(s):  
Zhifeng Guo ◽  
Kaile Zhou ◽  
Xiaoling Zhang ◽  
Shanlin Yang ◽  
Zhen Shao
Keyword(s):  
Data Mining ◽  
Electricity Consumption ◽  
Consumption Patterns

scholarly journals A Road Network Traffic State Identification Method Based on Macroscopic Fundamental Diagram and Spectral Clustering and Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaohui Lin
Keyword(s):  
Data Mining ◽  
Network Traffic ◽  
Spectral Clustering ◽  
Road Network ◽  
Clustering Algorithm ◽  
Support Vector ◽  
Mining Method ◽  
Traffic State ◽  
State Identification ◽  
Spectral Clustering Algorithm

Accurate identification of road network traffic status is the key to improve the efficiency of urban traffic control and management. Both data mining method and MFD-based methods can divide the traffic state of road network, but each has its own advantages and disadvantages. The data mining method is oriented to traffic data with high efficiency, but it can only discriminate traffic status from microlevel, while the MFD of road network can discriminate traffic status from macrolevel, but there are still some problems, such as the fact that the discriminant method of equivalence points based on MFD lacks theoretical support or that traffic status could not be subdivided. If data mining methods and road network’s MFD are combined, the accuracy of road network traffic state identification will be greatly improved. In addition, the research shows that the combination of unsupervised learning clustering analysis method (such as spectral clustering algorithm) and supervised learning machine algorithm (such as support vector machine algorithm (SVM)) is more accurate in traffic state identification. Therefore, a traffic state identification method based on MFD and spectral clustering and SVM is proposed, combining the advantages of spectral clustering algorithm and SVM algorithm. Firstly, spectral clustering algorithm is used to classify the traffic state of road network’s MFD. Secondly, SVM multiclassifier is trained with the partitioned road network’s MFD parameters, and the accuracy evaluation method of classification results based on obfuscation matrix is given. Finally, the connected-vehicle network simulation platform is built for empirical analysis. The results show that the classification results of spectral clustering algorithm are closer to the theoretical values, compared with K-means algorithm, and the accuracy of SVM multiclassifier is 96.3%. It can be seen that our algorithm can identify the road network traffic state more effectively from the macrolevel.

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