[논문]An Accelerated Edge Cloud System for Energy Data Stream Processing Based on Adaptive Incremental Deep Learning Scheme

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

doi:10.1109/access.2020.3033771

[해외논문] An Accelerated Edge Cloud System for Energy Data Stream Processing Based on Adaptive Incremental Deep Learning Scheme 원문보기

IEEE access : practical research, open solutions, v.8, 2020년, pp.195341 - 195358

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

Abstract ▼ AI-Helper

As smart metering technology evolves, power suppliers can make low-cost, low-risk estimation of customer-side power consumption by analyzing energy demand data collected in real-time. With advances network infrastructure, smart sensors, and various monitoring technologies, a standardized energy metering infrastructure, called advanced metering infrastructure (AMI), has been introduced and deployed to urban households to allow them to develop efficient power generation plans. Compared to traditional stochastic approaches for time-series data analysis, deep-learning methods have shown superior accuracy on many prediction applications. Because smart meters and infrastructure monitors produce a series of measurements over time, a large amount of data is accumulated, creating a large data stream, which takes much time from data generation to deployment of deep-learning model training. In this article, we propose an accelerated computing system that considers time-variant properties for accurate prediction of energy demand by processing the AMI stream data. The proposed system is a real-time training/inference system that deploys AMI data over a distributed edge cloud. It comprises two core components: an adaptive incremental learning solver and deep-learning acceleration with FPGA-GPU resource scheduling. An adaptive incremental learning scheme adjusts the batch/epoch in training iteration to reduce the time delay of the latest trained model, while trying to prevent biased-training due to the sub-optimal optimizer of incremental learning. In addition, a resource scheduling scheme manages various accelerator resources for accelerated deep-learning processing while minimizing the computational cost. The experimental results demonstrated that our method achieved good performance for adaptive batch size and epoch for incremental learning while guaranteeing a low inference error, a high model score, and queue stability with cost efficient processing.

참고문헌 (41)

10.1109/CVPR.2015.7298878
arXiv 1806 02920 GAIN: Missing data imputation using generative adversarial nets yoon 2018
Alahakoon, Damminda, Xinghuo Yu. Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey. IEEE transactions on industrial informatics, vol.12, no.1, 425-436.

상세보기
Adaptive workflow scheduling scheme based on the colored petri-net model in cloud kim 2014
arXiv 1609 04747 An overview of gradient descent optimization algorithms ruder 2016
10.1017/CBO9780511804441
Int J Eng Research & Development K-nearest neighbor in missing data imputation malarvizhi 2012 5 5
IEEE Trans Intell Transp Syst PPCA-based missing data imputation for traffic flow volume: A systematical approach qu 2009 10.1109/TITS.2009.2026312 10 512

상세보기
Kornelsen, Kurt, Coulibaly, Paulin. Comparison of Interpolation, Statistical, and Data-Driven Methods for Imputation of Missing Values in a Distributed Soil Moisture Dataset. Journal of hydrologic engineering, vol.19, no.1, 26-43.

상세보기
Chen, Wen, Zhou, Kaile, Yang, Shanlin, Wu, Cheng. Data quality of electricity consumption data in a smart grid environment. Renewable & sustainable energy reviews, vol.75, 98-105.

상세보기
Wang, Boyu, Pineau, Joelle. Online Bagging and Boosting for Imbalanced Data Streams. IEEE transactions on knowledge and data engineering, vol.28, no.12, 3353-3366.

상세보기
Huang, Chiou-Jye, Kuo, Ping-Huan. A Deep CNN-LSTM Model for Particulate Matter (PM _2.5 ) Forecasting in Smart Cities. Sensors, vol.18, no.7, 2220-.

상세보기
Proc Odyssey Cosine similarity scoring without score normalization techniques dehak 2010 15
Cheung, Yin-Wong, Lai, Kon S.. Lag Order and Critical Values of the Augmented Dickey-Fuller Test. Journal of business & economic statistics : a publication of the American Statistical Association, vol.13, no.3, 277-280.

상세보기
Rashed Mohassel, R., Fung, A., Mohammadi, F., Raahemifar, K.. A survey on Advanced Metering Infrastructure. International journal of electrical power & energy systems, vol.63, 473-484.

상세보기
Proc Int Conf Inf Commun Technol Converg (ICTC) Study on AMI system of KEPCO kim 2010 459
Domingos, P., Hulten, G.. A General Framework for Mining Massive Data Streams. Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol.12, no.4, 945-949.

상세보기
Bian, Tao, Jiang, Zhong-Ping. Reinforcement learning for linear continuous-time systems: an incremental learning approach. IEEE/CAA journal of automatica sinica, vol.6, no.2, 433-440.

상세보기
arXiv 1409 3809 The missing piece in complex analytics: Low latency, scalable model management and serving with velox crankshaw 2014
Proc 14th USENIX Symp Networked Syst Design Implement (NSDI) Clipper: A low-latency online prediction serving system crankshaw 2017 613
Bottou, Léon, Curtis, Frank E., Nocedal, Jorge. Optimization Methods for Large-Scale Machine Learning. SIAM review, vol.60, no.2, 223-311.

상세보기
10.1145/3097983.3098147
Kim, Tae-Young, Cho, Sung-Bae. Predicting residential energy consumption using CNN-LSTM neural networks. Energy : technologies, resources, reserves, demands, impact, conservation, management, policy, vol.182, 72-81.

상세보기
Proc Adv Neural Inf Process Syst Large scale online learning bottou 2004 217
An accelerated streaming data processing scheme based on cnn-lstm hybrid model in energy service platform bae 2019
Ramirez-Gallego, S., Krawczyk, B., Garcia, S., Wozniak, M., Herrera, F.. A survey on data preprocessing for data stream mining: Current status and future directions. Neurocomputing, vol.239, 39-57.

상세보기
Radiuk, Pavlo M.. Impact of Training Set Batch Size on the Performance of Convolutional Neural Networks for Diverse Datasets. Information technology and management science, vol.20, no.1, 20-24.

상세보기
Ross, G.J., Adams, N.M., Tasoulis, D.K., Hand, D.J.. Exponentially weighted moving average charts for detecting concept drift. Pattern recognition letters, vol.33, no.2, 191-198.

상세보기
10.1109/IJCNN.2013.6706768
10.1145/3267809.3267817
Kang, Dong-Ki, Youn, Chan-Hyun. Real-Time Control for Power Cost Efficient Deep Learning Processing With Renewable Generation. IEEE access : practical research, open solutions, vol.7, 114909-114922.

상세보기
Widmer, Gerhard, Kubat, Miroslav. Learning in the presence of concept drift and hidden contexts. Machine learning, vol.23, no.1, 69-101.

상세보기
Kemri power economy review lee 2016
10.1109/ISCA.2018.00012
10.1145/3020078.3021741
10.1109/FPT.2017.8280155
Inta, Ra, Bowman, David J., Scott, Susan M.. The “Chimera”: An Off-The-Shelf CPU/GPGPU/FPGA Hybrid Computing Platform. International journal of reconfigurable computing, vol.2012, 1-10.

상세보기
10.1145/2684746.2689060
10.23919/FPL.2017.8056850
10.1145/3190508.3190517
10.1109/CoolChips.2015.7158532

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