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

Author

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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)

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.

Suggested Citation

  • Zeyu Wang & Ravi S. Srinivasan, 2015. "Classification of Household Appliance Operation Cycles: A Case-Study Approach," Energies, MDPI, vol. 8(9), pages 1-15, September.
  • Handle: RePEc:gam:jeners:v:8:y:2015:i:9:p:10522-10536:d:56184
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    References listed on IDEAS

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    1. Pan Duan & Kaigui Xie & Tingting Guo & Xiaogang Huang, 2011. "Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques," Energies, MDPI, vol. 4(1), pages 1-12, January.
    2. Mastrullo, R. & Mauro, A.W. & Menna, L. & Palma, A. & Vanoli, G.P., 2014. "Transient model of a vertical freezer with door openings and defrost effects," Applied Energy, Elsevier, vol. 121(C), pages 38-50.
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    Cited by:

    1. Matteo Caldera & Asad Hussain & Sabrina Romano & Valerio Re, 2023. "Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform," Energies, MDPI, vol. 16(2), pages 1-23, January.
    2. Alqahtani, Bandar Jubran & Patiño-Echeverri, Dalia, 2019. "Combined effects of policies to increase energy efficiency and distributed solar generation: A case study of the Carolinas," Energy Policy, Elsevier, vol. 134(C).
    3. Juan M. Belman-Flores & Sergio Ledesma & Armando Gallegos-Muñoz & Donato Hernandez, 2017. "Thermal Simulation of the Fresh Food Compartment in a Domestic Refrigerator," Energies, MDPI, vol. 10(1), pages 1-14, January.

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