전통적 기계학습과 딥러닝을 활용한 에너지 구성요소 분해

Abstract

Energy disaggregation is the process of separating a households total electricity consumption into energy consumptions of individual appliances. Energy disaggregation is performed by applying a set of algorithms to aggregated electricity data. Energy disaggregation can be helpful for energy feedback, detection of appliance malfunctioning, energy incentive design, and demand-response management. In this thesis, we apply machine learning algorithms to energy disaggregation problem. Data were measured in 58 Japanese households. In our first study, we formulated energy disaggregation problem into on-off states classification of appliances. To solve the classification problem, we take two main approaches. One is traditional ML approach and the other is deep neural networks approach. In the former approach, we devised the 'edge' concept and extracted 59 features and used traditional ML algorithms such as logistic regression, support vector machine, and random forest. In the latter approach, we applied deep neural networks for automated feature learning. Experiments demonstrate that deep neural networks algorithms perform better than traditional ML approach for weak signature appliances. On the other hand, the traditional ML algorithm showed better performance for the appliances with strong signatures. These results imply that the algorithms should be selected according to the kinds of household appliances. The second study was an experiment on sensitivity to sampling rate. As the classification was done by extracting the pattern from the signatures, the sampling rate of aggregated data emerges as an important issue. This is because the degree to which signatures are revealed depends on the sampling rate. Our experiments studied how the performance of machine learning algorithms varies as the sampling rate changes. The results are different depending on the type of appliance, but showed that the performance of the algorithm is drastically dropped as the sampling rate is lowered to the sampling rate of once per 10 seconds. Experimental results showed that even at 1Hz, the on-off classification of 90 seconds window can perform well enough, which implies 1Hz is enough to use in the industrial settings.