XGBoost 회귀를 활용한 편의점 계약전력 예측 모델의 최적화에 대한 연구

• 영문명: A Study on the Optimization of a Contracted Power Prediction Model for Convenience Store using XGBoost Regression
• 발행기관: 한국IT서비스학회
• 저자명: 김상민(Sang Min Kim) 박찬권(Chankwon Park) 이지은(Ji-Eun Lee)
• 간행물 정보: 『한국IT서비스학회지』제21권 제4호, 91~103쪽, 전체 13쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2022.08.31

영문 초록

This study proposes a model for predicting contracted power using electric power data collected in real time from convenience stores nationwide. By optimizing the prediction model using machine learning, it will be possible to predict the contracted power required to renew the contract of the existing convenience store. Contracted power is predicted through the XGBoost regression model. For the learning of XGBoost model, the electric power data collected for 16 months through a real-time monitoring system for convenience stores nationwide were used. The hyperparameters of the XGBoost model were tuned using the GridesearchCV, and the main features of the prediction model were identified using the xgb.importance function. In addition, it was also confirmed whether the preprocessing method of missing values and outliers affects the prediction of reduced power. As a result of hyperparameter tuning, an optimal model with improved predictive performance was obtained. It was found that the features of power.2020.09, power.2021.02, area, and operating time had an effect on the prediction of contracted power. As a result of the analysis, it was found that the preprocessing policy of missing values and outliers did not affect the prediction result. The proposed XGBoost regression model showed high predictive performance for contract power. Even if the preprocessing method for missing values and outliers was changed, there was no significant difference in the prediction results through hyperparameters tuning.

목차

1. 서 론

2. 이론적 배경

3. 연구 방법

4. 실증분석

5. 결 론

참고문헌

키워드

XGBoost Regression Contract Power Prediction Model Convenience Store