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Energies 2015, 8(9), 10239-10259; doi:10.3390/en80910239

Data-Driven Baseline Estimation of Residential Buildings for Demand Response

Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 121-742, Korea
Omni System Co., Ltd., 172, Gwangnaru-ro, Seongdong-gu, Seoul 133-822, Korea
Part of this paper was presented to IEEE Smart Grid Communications Conference 2014. Park, S.; Ryu, S.; Choi, Y.; Kim, H. A framework for baseline load estimation in demand response: Data mining approach. In Proceedings of the 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), Venice, Italy, 3–6 November 2014; pp. 638–643.
Author to whom correspondence should be addressed.
Academic Editor: G .J. M. (Gerard) Smit
Received: 10 July 2015 / Revised: 25 August 2015 / Accepted: 7 September 2015 / Published: 17 September 2015
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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The advent of advanced metering infrastructure (AMI) generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL) estimation in demand response (DR) management. CBL plays a significant role in measurement and verification process, which quantifies the amount of demand reduction and authenticates the performance. The proposed data-driven baseline modeling is based on the unsupervised learning technique. Specifically we leverage both the self organizing map (SOM) and K-means clustering for accurate estimation. This two-level approach efficiently reduces the large data set into representative weight vectors in SOM, and then these weight vectors are clustered by K-means clustering to find the load pattern that would be similar to the potential load pattern of the DR event day. To verify the proposed method, we conduct nationwide scale experiments where three major cities’ residential consumption is monitored by smart meters. Our evaluation compares the proposed solution with the various types of day matching techniques, showing that our approach outperforms the existing methods by up to a 68.5% lower error rate. View Full-Text
Keywords: demand response (DR) management; analytics for energy data; data mining; residential buildings; smart meters; customer baseline load demand response (DR) management; analytics for energy data; data mining; residential buildings; smart meters; customer baseline load

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Park, S.; Ryu, S.; Choi, Y.; Kim, J.; Kim, H. Data-Driven Baseline Estimation of Residential Buildings for Demand Response. Energies 2015, 8, 10239-10259.

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