Earticle

초록

영어

As global energy consumption continues to rise, artificial intelligence-based models for efficient energy usage are emerging worldwide. Traditional learning approaches necessitate extensive datasets to create accurate models for predicting energy consumption. However, acquiring the vast amount of data required by these models has its limitations. Therefore, we propose a model for accurate energy consumption prediction using transfer learning, allowing effective modeling with smaller datasets. In this study, we present a deep learning model for energy consumption prediction, utilizing transfer learning on various energy domain datasets. We construct a base model using CNN-Seq2Seq with input variables such as heat consumption and meteorological data. For comparative evaluation, we employ SVR, XGBoost, LightGBM, Random Forest, LSTM, and Seq2Seq models, utilizing metrics like MAE, MSE, and R2 Score. We explore the impact of applying transfer learning on source domain data to four distinct target domain datasets, comparing results with and without transfer learning, as well as with other machine learning algorithm models. The findings demonstrate that applying transfer learning yields superior accuracy across all four target domain datasets. This suggests the potential of overcoming limitations in specific energy domains and obtaining meaningful results through the application of transfer learning.

목차

Abstract
I. INTRODUCTION
II. METHOD
A. Dataset
B. Deep Learning Model and Transfer Learning
III. RESULT
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Yunjae Kim [ Department of Computer Science & Engineering Sejong Univerity ]
• Sanghyun Ryu [ Department of Artificial Intelligence Sejong Univerity ]
• Jiwon Kim [ Department of Artificial Intelligence Sejong Univerity ]
• Sukjun Lee [ Department of information & industrial Engineering KwangWoon Univerity ]
• Hyeonjoon Moon [ Department of Computer Science & Engineering Sejong Univerity ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004