IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i20p8704-d431929.html
   My bibliography  Save this article

Utilizing Electricity Consumption Data to Assess the Noise Discomfort Caused by Electrical Appliances between Neighbors: A Case Study of a Campus Apartment Building

Author

Listed:
  • Do-Hyeon Ryu

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea)

  • Ryu-Hee Kim

    (Planning & Service, AUTOCRYPT, 25 Gukjegeumyoung-ro 2-gil, Yeongdeungpo-gu, Seoul 07327, Korea)

  • Seung-Hyun Choi

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea)

  • Kwang-Jae Kim

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea
    Open Innovation Big Data Center, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea)

  • Young Myoung Ko

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea
    Open Innovation Big Data Center, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea)

  • Young-Jin Kim

    (Open Innovation Big Data Center, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea
    Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea)

  • Minseok Song

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea
    Open Innovation Big Data Center, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea)

  • Dong Gu Choi

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea
    Open Innovation Big Data Center, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyung-buk 37673, Korea)

Abstract

Real-time collection of household electricity consumption data has been facilitated by an advanced metering infrastructure. In recent studies, collected data have been processed to provide information on household appliance usage. The noise caused by electrical appliances from neighboring households constitutes a major issue, which is related to discomfort and even mental diseases. The assessment of noise discomfort using electricity consumption data has not been dealt with in the literature up to this day. In this study, a method that utilizes electricity consumption data for the assessment of noise discomfort levels caused by electrical appliances between neighboring households is proposed. This method is based on the differences in the usage time of electrical appliances in a collective residential building. The proposed method includes the following four steps: data collection and preprocessing, residential units clustering, noise discomfort modeling, and evaluation of noise discomfort. This method is demonstrated through a case study of a campus apartment building. Variations in the noise discomfort assessment model and measures for alleviating noise discomfort are also discussed. The proposed method can guide the application of electricity consumption data to the assessment and alleviation of noise discomfort from home appliances at an apartment building.

Suggested Citation

  • Do-Hyeon Ryu & Ryu-Hee Kim & Seung-Hyun Choi & Kwang-Jae Kim & Young Myoung Ko & Young-Jin Kim & Minseok Song & Dong Gu Choi, 2020. "Utilizing Electricity Consumption Data to Assess the Noise Discomfort Caused by Electrical Appliances between Neighbors: A Case Study of a Campus Apartment Building," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8704-:d:431929
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/20/8704/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/20/8704/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    2. Yilmaz, S. & Chambers, J. & Patel, M.K., 2019. "Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management," Energy, Elsevier, vol. 180(C), pages 665-677.
    3. Zhuang Zheng & Hainan Chen & Xiaowei Luo, 2018. "A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances," Sustainability, MDPI, vol. 10(4), pages 1-28, March.
    4. Szu-Cheng Lu & Keh-Chin Chang & Wei-Min Lin & Kung-Ming Chung, 2015. "Energy Usage of Residents on Offshore Islands in Taiwan," Sustainability, MDPI, vol. 7(6), pages 1-13, May.
    5. Median Mutiara, 2018. "Noise Complaints between Japanese Neighbors and Migrants in Rural Japan: From the Perspectives of Noisemakers," Social Sciences, MDPI, vol. 7(12), pages 1-25, December.
    6. José Montoya-Belmonte & Juan M. Navarro, 2020. "Long-Term Temporal Analysis of Psychoacoustic Parameters of the Acoustic Environment in a University Campus Using a Wireless Acoustic Sensor Network," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    7. Shuxin Mao & Sha Qiu & Tao Li & Mingfang Tang & Hongbing Deng & Hua Zheng, 2020. "Using Characteristic Energy to Study Rural Ethnic Minorities’ Household Energy Consumption and Its Impact Factors in Chongqing, China," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    8. Li, Kehua & Ma, Zhenjun & Robinson, Duane & Ma, Jun, 2018. "Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering," Applied Energy, Elsevier, vol. 231(C), pages 331-342.
    9. Stankovic, L. & Stankovic, V. & Liao, J. & Wilson, C., 2016. "Measuring the energy intensity of domestic activities from smart meter data," Applied Energy, Elsevier, vol. 183(C), pages 1565-1580.
    10. Sanquist, Thomas F. & Orr, Heather & Shui, Bin & Bittner, Alvah C., 2012. "Lifestyle factors in U.S. residential electricity consumption," Energy Policy, Elsevier, vol. 42(C), pages 354-364.
    11. Hume, Ken & Gregg, Martin & Thomas, Callum & Terranova, Daniela, 2003. "Complaints caused by aircraft operations: an assessment of annoyance by noise level and time of day," Journal of Air Transport Management, Elsevier, vol. 9(3), pages 153-160.
    Full references (including those not matched with items on IDEAS)

    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. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
    2. Gianluca Trotta & Kirsten Gram-Hanssen & Pernille Lykke Jørgensen, 2020. "Heterogeneity of Electricity Consumption Patterns in Vulnerable Households," Energies, MDPI, vol. 13(18), pages 1-17, September.
    3. De Lauretis, Simona & Ghersi, Frédéric & Cayla, Jean-Michel, 2017. "Energy consumption and activity patterns: An analysis extended to total time and energy use for French households," Applied Energy, Elsevier, vol. 206(C), pages 634-648.
    4. Jieyi Kang & David Reiner, 2021. "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Working Papers EPRG2113, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    5. Grillone, Benedetto & Mor, Gerard & Danov, Stoyan & Cipriano, Jordi & Sumper, Andreas, 2021. "A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings," Applied Energy, Elsevier, vol. 301(C).
    6. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    7. Mishra, Kakuli & Basu, Srinka & Maulik, Ujjwal, 2022. "Load profile mining using directed weighted graphs with application towards demand response management," Applied Energy, Elsevier, vol. 311(C).
    8. Michalakopoulos, Vasilis & Sarmas, Elissaios & Papias, Ioannis & Skaloumpakas, Panagiotis & Marinakis, Vangelis & Doukas, Haris, 2024. "A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs," Applied Energy, Elsevier, vol. 361(C).
    9. Lesley Thomson & David Jenkins, 2023. "The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets," Energies, MDPI, vol. 16(16), pages 1-29, August.
    10. Jieyi Kang & David Reiner, 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Working Papers EPRG2114, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    11. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
    12. Li, Tong & Wang, Zhaohua & Zhao, Wenhui, 2022. "Comparison and application potential analysis of autoencoder-based electricity pattern mining algorithms for large-scale demand response," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    13. Li, Kehua & Yang, Rebecca Jing & Robinson, Duane & Ma, Jun & Ma, Zhenjun, 2019. "An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library b," Energy, Elsevier, vol. 174(C), pages 735-748.
    14. Troy Malatesta & Jessica K. Breadsell, 2022. "Identifying Home System of Practices for Energy Use with K-Means Clustering Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    15. Walker, Shalika & Bergkamp, Vince & Yang, Dujuan & van Goch, T.A.J. & Katic, Katarina & Zeiler, Wim, 2021. "Improving energy self-sufficiency of a renovated residential neighborhood with heat pumps by analyzing smart meter data," Energy, Elsevier, vol. 229(C).
    16. Roberts, Mike B. & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2019. "Characterisation of Australian apartment electricity demand and its implications for low-carbon cities," Energy, Elsevier, vol. 180(C), pages 242-257.
    17. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    18. Cansino, José M. & Dugo, Víctor & Gálvez-Ruiz, David & Román-Collado, Rocío, 2023. "What drove electricity consumption in the residential sector during the SARS-CoV-2 confinement? A special focus on university students in southern Spain," Energy, Elsevier, vol. 262(PB).
    19. Liu, Benxi & Liu, Tengyuan & Liao, Shengli & Lu, Jia & Cheng, Chuntian, 2023. "Short-term coordinated hybrid hydro-wind-solar optimal scheduling model considering multistage section restrictions," Renewable Energy, Elsevier, vol. 217(C).
    20. Violeta Mihaela Dincă & Mihail Busu & Zoltan Nagy-Bege, 2022. "Determinants with Impact on Romanian Consumers’ Energy-Saving Habits," Energies, MDPI, vol. 15(11), pages 1-18, June.

    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:jsusta:v:12:y:2020:i:20:p:8704-:d:431929. 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.