• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.7
• /
• pp.827-833
• /
• 2020

In this paper, to reduce the unpaid rate of local governments, the internal data elements affecting the arrears in Water-INFOS are searched through interviews with meter readers in certain local governments. Candidate data affecting arrears from national statistical data were derived. The influence of the independent variable on the dependent variable was sampled by examining the disorder of the dependent variable in the data set called information gain. We also evaluated the higher prediction rates of decision tree and logistic regression using n-fold cross-validation. The results confirmed that the decision tree can find more accurate customer payment patterns than logistic regression. In the process of developing an analysis algorithm model using machine learning, the optimal values of two environmental variables, the minimum number of data and the maximum purity, which directly affect the complexity and accuracy of the decision tree, are derived to improve the accuracy of the algorithm.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.05a
• /
• pp.250-252
• /
• 2019

국내의 전력 산업은 부정확한 전력수요 예측으로 전력부족과 공급과잉의 주기적 반복이 발생하여 전력 과생산, 에너지 낭비, 전력 과소비와 요금 체납 등의 문제가 발생하고 있다. 이를 해결하기 위해 본 논문에서는 LSTM 알고리즘을 사용하여 전력사용량 예측하고, 정량의 전력을 선구입 할 수 있도록 설계하였다. 제안하는 시스템은 스마트그리드 환경과 인공지능으로 정량의 전기를 구입 할 수 없는 기존의 전력 산업 문제를 보완하여 소비자의 전기요금 절감과 에너지 절약이 가능하다.