IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i9p10522-10536d56184.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/9/10522/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/9/10522/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    2. Huang, Tao & Bacher, Peder & Møller, Jan Kloppenborg & D’Ettorre, Francesco & Markussen, Wiebke Brix, 2023. "A step towards digital operations—A novel grey-box approach for modelling the heat dynamics of ultra-low temperature freezing chambers," Applied Energy, Elsevier, vol. 349(C).
    3. Ebrahim Farjah & Mosayeb Bornapour & Taher Niknam & Bahman Bahmanifirouzi, 2012. "Placement of Combined Heat, Power and Hydrogen Production Fuel Cell Power Plants in a Distribution Network," Energies, MDPI, vol. 5(3), pages 1-25, March.
    4. Csereklyei, Zsuzsanna & Thurner, Paul W. & Langer, Johannes & Küchenhoff, Helmut, 2017. "Energy paths in the European Union: A model-based clustering approach," Energy Economics, Elsevier, vol. 65(C), pages 442-457.
    5. Harrington, Lloyd & Aye, Lu & Fuller, Bob, 2018. "Impact of room temperature on energy consumption of household refrigerators: Lessons from analysis of field and laboratory data," Applied Energy, Elsevier, vol. 211(C), pages 346-357.
    6. Borges, Bruno N. & Melo, Cláudio & Hermes, Christian J.L., 2015. "Transient simulation of a two-door frost-free refrigerator subjected to periodic door opening and evaporator frosting," Applied Energy, Elsevier, vol. 147(C), pages 386-395.
    7. Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search," Energies, MDPI, vol. 9(10), pages 1-17, October.
    8. Liu, Xin & Zhang, Zijun & Song, Zhe, 2020. "A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    9. Alzuwaid, F.A. & Ge, Y.T. & Tassou, S.A. & Sun, J., 2016. "The novel use of phase change materials in an open type refrigerated display cabinet: A theoretical investigation," Applied Energy, Elsevier, vol. 180(C), pages 76-85.
    10. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:8:y:2015:i:9:p:10522-10536:d:56184. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.