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Energies 2015, 8(9), 10522-10536; doi:10.3390/en80910522

Classification of Household Appliance Operation Cycles: A Case-Study Approach

1
Rinker, Sr. School of Construction Management, 324 Rinker Hall, University of Florida, Gainesville, FL 32611, USA
2
Rinker, Sr. School of Construction Management, 316 Rinker Hall, University of Florida, Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Nyuk Wong
Received: 8 July 2015 / Revised: 10 September 2015 / Accepted: 17 September 2015 / Published: 22 September 2015
(This article belongs to the Special Issue Energy Efficient Building Design 2016)
View Full-Text   |   Download PDF [446 KB, uploaded 22 September 2015]   |  

Abstract

In recent years, a new generation of power grid system, referred to as the Smart Grid, with an aim of managing electricity demand in a sustainable, reliable, and economical manner has emerged. With greater knowledge of operational characteristics of individual appliances, necessary automation control strategies can be developed in the Smart Grid to operate appliances in an efficient manner. This paper provides a way of classifying different operational cycles of a household appliance by introducing an unsupervised learning algorithm called k-means clustering. An intrinsic method known as silhouette coefficient was used to measure the classification quality. An identification process is also discussed in this paper to help users identify the operation mode each types of operation cycle stands for. A case study using a typical household refrigerator is presented to validate the proposed method. Results show that the proposed the classification and identification method can partition and identify different operation cycles adequately. Classification of operation cycles for such appliances is beneficial for Smart Grid as it provides a clear and convincing understanding of the operation modes for effective power management. View Full-Text
Keywords: smart grid; refrigerator operation cycles; k-means clustering; unsupervised learning smart grid; refrigerator operation cycles; k-means clustering; unsupervised learning
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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MDPI and ACS Style

Wang, Z.; Srinivasan, R.S. Classification of Household Appliance Operation Cycles: A Case-Study Approach. Energies 2015, 8, 10522-10536.

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