개인 맞춤형 에너지 피드백 시스템을 위한 딥러닝 기반의 다수 가전기기 유효전력 분해
Deep Learning Based Active Power Disaggregation of Appliances for Personalized Energy Feedback System
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초록
Energy consumption feedback with an appliance-level feedback system can reduce consumption by a maximum of 12%. In this study, we proposed a data acquisition and training framework for configuring deep learning based on Non-Intrusive Load Monitoring (NILM) for a personalized and appliance-level energy consumption feedback system. To construct a training dataset, an aggregation of active power data from four types of home appliances (refrigerator, induction, TV, washing machine) was performed for approximately three weeks. LSTNet was applied to extract and recognize the features of active power data and the state of each home appliance. With an accuracy metric of more than 90% of the disaggregation result, the applicability of the appliance-level active power feedback system was verified.
Abstract
Energy consumption feedback with an appliance-level feedback system can reduce consumption by a maximum of 12%. In this study, we proposed a data acquisition and training framework for configuring deep learning based on Non-Intrusive Load Monitoring (NILM) for a personalized and appliance-level energy consumption feedback system. To construct a training dataset, an aggregation of active power data from four types of home appliances (refrigerator, induction, TV, washing machine) was performed for approximately three weeks. LSTNet was applied to extract and recognize the features of active power data and the state of each home appliance. With an accuracy metric of more than 90% of the disaggregation result, the applicability of the appliance-level active power feedback system was verified.
참고문헌 (25)
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