전이 학습 기반 고정된 레이어 수에 따른 건물 전력 소비량 예측 정확도 검증
Validation of Building Power Consumption Prediction Accuracy According to Frozen Number of Layers based on Transfer Learning
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Owing to recent advances in data-driven technologies, prediction models can now be utilized in various fields such as energy consumption prediction. Among them, transfer learning, which enables prediction using data collected for a short term, is actively gaining ground. In the field of building energy, attention is focused on predicting the power consumption of buildings with the development of smart grids and micro-grids. The purpose of this study is to implement a machine learning (ML) method using the transfer learning algorithm that enables the prediction of energy consumption of the buildings using data collected for a short-term period. This study also validates the accuracy of the predicted energy consumption by adjusting the number of frozen layers based on the input parameters used in the ML method. A total of 5 layers were set, and the frozen layers were increased from 0 to 5. In addition, to validate the prediction rate of the transfer learning algorithm, only the existing ML model was used. As a result, when 0 through 3 layers were fixed, the accuracy criterion tended to be met. In contrast, when the 4-5 layers were reflected, the accuracy criterion was not met. It should also be noted that the model without using transfer learning showed poor accuracy.
Abstract
Owing to recent advances in data-driven technologies, prediction models can now be utilized in various fields such as energy consumption prediction. Among them, transfer learning, which enables prediction using data collected for a short term, is actively gaining ground. In the field of building energy, attention is focused on predicting the power consumption of buildings with the development of smart grids and micro-grids. The purpose of this study is to implement a machine learning (ML) method using the transfer learning algorithm that enables the prediction of energy consumption of the buildings using data collected for a short-term period. This study also validates the accuracy of the predicted energy consumption by adjusting the number of frozen layers based on the input parameters used in the ML method. A total of 5 layers were set, and the frozen layers were increased from 0 to 5. In addition, to validate the prediction rate of the transfer learning algorithm, only the existing ML model was used. As a result, when 0 through 3 layers were fixed, the accuracy criterion tended to be met. In contrast, when the 4-5 layers were reflected, the accuracy criterion was not met. It should also be noted that the model without using transfer learning showed poor accuracy.
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