scholarly journals An Improved Decision Tree Algorithm for Electricity Theft Prediction and Analysis

2020 ◽  
Author(s):  
Xiaofeng Chen ◽  
Lipeng Zhang ◽  
Zhongping Xu ◽  
Feng Zhu ◽  
Xiaoming Qi
Keyword(s):  
Decision Tree ◽  
Information Acquisition ◽  
Strong Support ◽  
Information Value ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Electricity Theft ◽  
Grid Operation ◽  
Financial Losses ◽  
Staff Workload

At present, there are many kinds of electricity theft and the corresponding approaches to combat this are insufficient. Manual approaches result in a heavy staff workload and are inefficient. In this paper, the data from an electricity information acquisition system is collected and mined using Python. Based on an understanding of the business and an analysis of the information value (IV) measure, important characteristic indexes are selected and an improved decision tree algorithm is used to construct a model of power theft by users. This method effectively narrows the range of users suspected of power theft, improving the pertinence of audit, and providing strong support for reducing the financial losses of power supply enterprises and ensuring the safety of power grid operation.

scholarly journals Distinction Between Natural and Artificial Agarwood Based on Electronic Nose Data

2020 ◽  
Author(s):  
Shenghong Wu ◽  
Yueqian Huang ◽  
Xiaoying Chen ◽  
Xin Zhou ◽  
Weimin Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Electronic Nose ◽  
Principal Component ◽  
Information Value ◽  
Chemical Markers ◽  
Decision Tree Algorithm ◽  
Chemical Marker ◽  
Tree Algorithm ◽  
Ancient Times

Abstract Background: Agarwood is widely used as a traditional medicine all over the world. Distinction between the qualities of natural and artificial agarwood is a current hot research topic among agarwood research communities. An important sensory characteristic of agarwood lies in its incense smoke, and an analysis of incense smoke has been traditionally used to evaluate the agarwood quality since ancient times. The aim of this study is to establish a rapid detection method using electronic nose (E-nose) systems to distinguish between natural and artificial agarwood. Result: Incense smokes of 45 natural and artificial agarwood samples were analyzed by E-nose, and principal component analysis (PCA) was employed to cluster the E-nose data. The chemical markers which could be used to distinguish between artificial and natural agarwood were identified by GC-MS combined with information value and decision tree algorithm. The results showed that the smellprints of artificial agarwood contained more peaks, while those of natural agarwood had higher response intensities. The compounds that were different between the two types of agarwood were three sesquiterpenes and six chromone derivatives. The result from decision tree algorithm further showed that 6-hydroxy-2-(2-phenylethyl)chromone was the chemical marker that could be used to distinguish between artificial and natural agarwood. Nootkatone and 2-(2-phenylethyl)chromone were the chemical markers that may contributed to the clustering of the E-nose data; the two compounds can be used to evaluate the incense smoke of agarwood. Conclusion: We demonstrated that our developed E-nose-based method could rapidly distinguish between the incense smokes of artificial and natural agarwood; this method could be applied to evaluate the quality of agarwood in the future.

scholarly journals THEFT DETECTION IN POWER UTILITIES USING ENSEMBLE OF CHAID DECISION TREE ALGORITHM

2020 ◽  
Vol 2 (2) ◽  
pp. 161-165
Author(s):  
Muhammad Salman Saeed ◽  
Mohd. Wazir Mustafa ◽  
Usman Ullah Sheikh ◽  
Attaullah Khidrani ◽  
Mohd Norzali Haji Mohd
Keyword(s):  
Decision Tree ◽  
Distribution System ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Decision Tree Algorithm ◽  
Chi Square ◽  
Tree Algorithm ◽  
Interaction Detection ◽  
Electricity Theft

Theft of electricity and fraud in energy consumption billing are the primary concerns for Distribution System Operators . Because of those illegal activities, it is believed that billions of dollars are wasted each year. DSOs around the world continue to use conventional time consuming and inefficient methods for non-technical loss detection, particularly in underdeveloped countries . This research work attempts to solve the problems as mentioned above by designing an effective model for detecting electricity theft to classify fraudster customers in a power delivery system. The key motivation for this current study is to support the DSOs in their fight against the stealing of electricity. Initially, the proposed method uses the monthly energy customer consumption data obtained from Multan Electric Power Company (MEPCO) Pakistan to segregate fraudsters and honest customers. The Bagged Chi - square Automatic Interaction Detection (CHAID) based decision tree algorithm is then used to classify the honest and fraudster consumers.Furthermore, based on the mentioned metrics, the performance superiority of the Bagged CHAID-based NTL detection method is validated by comparing its efficacy with that of few well-known state-of-the-art machine learning algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM),) Logistic Regression (LR), Discriminant Analysis and Bayesian Network (BN).

scholarly journals Classification of diabetes disease using decision tree algorithm (C4.5)

2021 ◽  
Vol 1869 (1) ◽  
pp. 012082
Author(s):  
B A C Permana ◽  
R Ahmad ◽  
H Bahtiar ◽  
A Sudianto ◽  
I Gunawan
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithm
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