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

Zeyu Wang and Ravi S. Srinivasan
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Zeyu Wang: Rinker, Sr. School of Construction Management, 324 Rinker Hall, University of Florida, Gainesville, FL 32611, USA
Ravi S. Srinivasan: Rinker, Sr. School of Construction Management, 316 Rinker Hall, University of Florida, Gainesville, FL 32611, USA

Energies, 2015, vol. 8, issue 9, 1-15

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.

Keywords: smart grid; refrigerator operation cycles; k-means clustering; unsupervised learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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