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

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-11
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.8, pp.199120 - 199132

ISSN
2169-3536
DOI
10.1109/access.2020.3035421
URI
http://hdl.handle.net/10203/277214
Appears in Collection
EE-Journal Papers(저널논문)
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