Abstract
This paper designs an in-home display capable of integratively coordinating power management activities from diverse smart grid entities and presents its implementation details. With IHDs, the power consumption values are captured at fixed time intervals by smart meters and sent to the network operation center through an end-to-end connection embracing Zigbee, WLAN, and the Internet. Built upon request-and-response semantic, a control path is implemented from IHDs to smart sockets. This path is extended to smart phones from IHDs, making it possible for customers to send a command or receive the current status of respective appliances on their phones. The high-capacity data server belonging to the network operation center manages and analyzes the time-series metering data sets for accurate demand forecasting using artificial neural networks. After all, our framework can integrate sophisticated power consumption scheduler and automatically send the control command according to a specific schedule.
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References
Korean Smart Grid Institute. http://www.smartgrid.or.kr/eng.htm
Luan, S., Teng, J., Chan, S., Hwang, L.: Development of a smart power meter for AMI based on ZigBee communication. In: Power Electronics and Drive Systems, pp. 661–665 (2009)
Bonneville, E., Rialhe, A.: Demand side management for residential and commercial end-users. www.leonardo-energy.org/Files/DSM-commerce.pdf (2006)
Abras, S., Pesty, S., Ploix, S., Jacomino, M.: An anticipation mechanism for power management in a smart home using multi-agent systems. In: 3rd International Conference on From Theory to Applications, pp. 1–6 (2008)
Lee, J., Kim, H., Park, G., Kang, M.: Energy consumption scheduler for demand response systems in the smart grid. J. Inf. Sci. Eng. 28(5), 955–969 (2012)
Lee, J., Park, G., Kwak, F., Jeon, H.: Power consumption scheduling based on genetic algorithms for smart places. In: Jedrzejowicz, P., et al. (eds.) ICCCI 2011, LNCS, vol. 6922, pp. 438–447. Springer, Heidelberg (2011)
Silva, D., Yu, X., Alahakoon, D., Holmes, G.: Semi-supervised classification of characterized patterns for demand forecasting using smart electricity meters. In: International Conference on Electrical Machines and Systems, pp. 1–6 (2011)
Nissen, S.: Neural Network Made Simple. Software 2.0 (2005)
Tan, Y., Liu, W., Qiu, Q.: Adaptive power management using reinforcement learning. In: IEEE/ACM International Conference on Computer-Aided Design, pp. 461–467 (2009)
Acknowledgments
This research was supported by the MKE (The Ministry of Knowledge Economy), Republic of Korea, under IT/SW Creative research program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2012-(H0502-12-1002))
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Lee, J., Park, GL., Han, J. (2013). Integrative In-Home Display Development for Smart Places. In: Han, YH., Park, DS., Jia, W., Yeo, SS. (eds) Ubiquitous Information Technologies and Applications. Lecture Notes in Electrical Engineering, vol 214. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5857-5_39
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DOI: https://doi.org/10.1007/978-94-007-5857-5_39
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