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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
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    References listed on IDEAS

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    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.
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