닫기
Loading..

전자정보연구정보센터 ICT 융합 전문연구정보의 집대성

영문 논문지

홈 홈 > 연구문헌 > 영문 논문지 > IEIE Transactions on Smart Processing & Computing (IEIE SPC)

IEIE Transactions on Smart Processing & Computing (IEIE SPC)

Current Result Document : 0 / 0

한글제목(Korean Title) Effective Electricity Demand Prediction via Deep Learning
영문제목(English Title) Effective Electricity Demand Prediction via Deep Learning
저자(Author) Daegun Ko   Youngmin Yoon   Jinoh Kim   Haelyong Choi  
원문수록처(Citation) VOL 10 NO. 06 PP. 0483 ~ 0489 (2021. 12)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
Prediction of electricity demand in homes and buildings can be used to optimize an energy management system by decreasing energy wastage. A time-series prediction system is still a challenging problem in machine learning and deep learning. Our main idea is to compare three methods. For this work, we analyzed an electricity demand prediction system using the current state-of-the-art deep-learning methods with a machine-learning method: error correction with multi-layer perceptron (eMLP) structure, autoregressive integrated moving average (ARIMA) structure, and a proposed structure named CNN-LSTM. For this, we measured and collected electricity demand data in Germany for home appliances. We report the prediction accuracy in terms of the mean square error (MSE) and mean absolute percentage error (MAPE). The experimental result indicates that CNN-LSTM outperforms eMLP and ARIMA in accuracy.
키워드(Keyword) LSTM   CNN   Electricity demand prediction   Deep-learning   Machine-learning   ARIMA   MLP  
원문 PDF 다운로드