earticle

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.