[논문]Cooperating Edge Cloud-Based Hybrid Online Learning for Accelerated Energy Data Stream Processing in Load Forecasting

Lee, Changha; Kim, Seong-Hwan; Youn, Chan-Hyun

doi:10.1109/access.2020.3035421

[해외논문] Cooperating Edge Cloud-Based Hybrid Online Learning for Accelerated Energy Data Stream Processing in Load Forecasting 원문보기

IEEE access : practical research, open solutions, v.8, 2020년, pp.199120 - 199132

Lee, Changha (Korea Advanced Institute of Science and Technology, School of Electrical Engineering, Daejeon, South Korea) , Kim, Seong-Hwan (Korea Advanced Institute of Science and Technology, School of Electrical Engineering, Daejeon, South Korea) , Youn, Chan-Hyun (Korea Advanced Institute of Science and Technology, School of Electrical Engineering, Daejeon, South Korea)

Abstract ▼ AI-Helper

The data analysis platform used in smart grid is important to provide more accurate data validation and advanced power services. Recently, the researches based on deep neural network have been increasing in data analytic platforms to address various problems using artificial intelligence. The main problem to analyze multiple meter data based on deep learning is that the data distribution is varying according to both different client and time flow. Some studies, such as continual learning, are effective in dynamically fluctuating data distributions, but require additional complex computational procedures that make it difficult to construct an online learning system for processing data streams. In this paper, we proposed a hybrid deep learning scheduling algorithm to improve accuracy and accelerate learning performance in a multiple smart meter source environment, of which biased data feature varies dynamically. We use a simple analysis method, cosine similarity, to reduce computation complexity. By analyzing the frequency distribution of cosine similarity, a model recognizes that biased data feature of power consumption patterns. The skewed data distribution is reduced by using the zero skewness property of of an uniform distribution. The diversity of memory buffer was increased by update strategy which maximizes variance of pattern. When scheduling an online and offline gradient in different computational complexity, the proposed model reduces processing time by selectively calculating gradient considering the degree of data feature transition. To verify the performance of the proposed algorithm, we conducted three experiments with AMI stream data on the proposed method and the existing method of online learning. The experimental results demonstrate that our method can achieve reasonable performance in terms of trade-off between accuracy and processing time.

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