Browse > Article
http://dx.doi.org/10.6109/jkiice.2018.22.4.587

Web based Customer Power Demand Variation Estimation System using LSTM  

Seo, Duck Hee (Department of Mobile Software, Sangmyung University)
Lyu, Joonsoo (Department of Electrical Engineering, Sangmyung University)
Choi, Eun Jeong (Department of Computer Science, Sangmyung University)
Cho, Soohwan (Department of Electrical Engineering, Sangmyung University)
Kim, Dong Keun (Department of Intelligent Enginnering Informatics for human, Sangmyung University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.4, 2018 , pp. 587-594 More about this Journal
Abstract
The purpose of this study is to propose a power demand volatility evaluation system based on LSTM and not to verify the accuracy of the demand module which is a core module, but to recognize the sudden change of power pattern by using deeplearning in the actual power demand monitoring system. Then we confirm the availability of the module. Also, we tried to provide a visualized report so that the manager can determine the fluctuation of the power usage patten by applying it as a module to the web based system. It is confirmed that the power consumption data shows a certain pattern in the case of government offices and hospitals as a result of implementation of the volatility evaluation system. On the other hand, in areas with relatively low power consumption, such as residential facilities, it was not appropriate to evaluate the volatility.
Keywords
Deep Learning; Power Demand Variation Estimation; LSTM; Electronic Control; Smart Grid;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 B. J. Jang and S. G. Han, "Energy-IT fusion technology trends and major issues," Communications of the Korean Institute of Information Scientists and Engineers, vol. 28, no. 7, pp. 44-51, Aug. 2010.
2 E. S. Yang, A. R. Kim, B. A. Kim and B. R. Shin, World Energy Market Insight 1st ed. Ulsan: Korea energy economics institute, 2016.
3 M. J. Sung and K. W. Shin, "A Small-area Hardware Implementation of EGML-based Moving Object Detection Processor," Journal of Korea Institute of Information and Communication Engineering, vol. 21, no. 12, pp.2213-2220, Dec. 2017.   DOI
4 J. Y. Eum, "A Study on the Development of Energy Supply and Demand Prediction Models for Smart City Energy Management System(CEMS)," MS. thesis, Sangmyung University, Seoul, 2015.
5 J. K. Ko, I. K. Yang and I. H. Yu, "A Study On Demand Pattern Analysis for Forecasting of Customer's Electricity Demand," International Journal of Information and Communication Engineering, vol. 9, no. 4, pp. 358-362, Aug. 2011.
6 N. H. Jo, " SVM Load Forecasting using Cross-Validation," The Transactions of The Korean Institute of Electrical Engineers , Vol. 55A No.11, pp. 485-491, Nov. 2006.
7 C. H. Kim, "Power demand forecasting model using mixed cycle data," Korea energy economics institute: KR, Research Report, 2014.
8 S. H. Song, "The Emotion Analysis Based on Long Short Term Memory using the Central and Autonomic Nervous System Signals," MS thesis Sangmyung University, Seoul, 2018.
9 Y. Sugomori, Java Deep Learning Essentials 1st ed. Birmingham, WD: Packt Publishing Ltd, 2016.
10 Skymind. Deeplearning4j: Open-source, Distributed Deep Learning for the JVM [Internet]. Available: https://deeplearning4j.org/quickref.
11 M. Mutingi, C. Mbohwa and P. dube, "System dynamics archetypes for capacity management of energy systems," in 4th International Conference on Power and Energy Systems Engineering, Berlin: DE, pp. 199-205, 2017.
12 J. Y. Lee and L. Kolasani, "Security Based Network for Health Care System," Asia-pacific Journal of Convergent Research Interchange, vol. 1, no. 1, pp. 1-6, Mar 2015.