IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v115y2014icp384-393.html
   My bibliography  Save this article

Using fuzzy logic to model the behavior of residential electrical utility customers

Author

Listed:
  • Zúñiga, K.V.
  • Castilla, I.
  • Aguilar, R.M.

Abstract

Peaks and valleys affecting the efficiency of the power system can be detected by analyzing the load curve. These oscillations are caused by changes in consumer behavior, mainly consumers in the residential sector. This paper presents the use of fuzzy logic systems to model human behavior related to activation of appliances and lighting at home. Based on this model, the hourly activation profile for each appliance can be obtained and, subsequently, the load curve of the residential sector can be calculated. This model aims at contributing to the simulation of strategies for demand-side management.

Suggested Citation

  • Zúñiga, K.V. & Castilla, I. & Aguilar, R.M., 2014. "Using fuzzy logic to model the behavior of residential electrical utility customers," Applied Energy, Elsevier, vol. 115(C), pages 384-393.
    • Handle: RePEc:eee:appene:v:115:y:2014:i:c:p:384-393
    DOI: 10.1016/j.apenergy.2013.11.030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261913009240
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2013.11.030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Train, Kenneth & Herriges, Joseph & Windle, Robert, 1985. "Statistically adjusted engineering (SAE) models of end-use load curves," Energy, Elsevier, vol. 10(10), pages 1103-1111.
    2. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    3. Grandjean, A. & Adnot, J. & Binet, G., 2012. "A review and an analysis of the residential electric load curve models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6539-6565.
    4. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    5. Mamlook, Rustum & Badran, Omar & Abdulhadi, Emad, 2009. "A fuzzy inference model for short-term load forecasting," Energy Policy, Elsevier, vol. 37(4), pages 1239-1248, April.
    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. El-Baz, Wessam & Tzscheutschler, Peter, 2015. "Short-term smart learning electrical load prediction algorithm for home energy management systems," Applied Energy, Elsevier, vol. 147(C), pages 10-19.
    2. Stenner, Karen & Frederiks, Elisha R. & Hobman, Elizabeth V. & Cook, Stephanie, 2017. "Willingness to participate in direct load control: The role of consumer distrust," Applied Energy, Elsevier, vol. 189(C), pages 76-88.
    3. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
    4. Di Giorgio, Alessandro & Liberati, Francesco, 2014. "Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models," Applied Energy, Elsevier, vol. 128(C), pages 119-132.
    5. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
    6. Nie, S. & Huang, Charley Z. & Huang, G.H. & Li, Y.P. & Chen, J.P. & Fan, Y.R. & Cheng, G.H., 2016. "Planning renewable energy in electric power system for sustainable development under uncertainty – A case study of Beijing," Applied Energy, Elsevier, vol. 162(C), pages 772-786.
    7. Salata, Ferdinando & Golasi, Iacopo & di Salvatore, Maicol & de Lieto Vollaro, Andrea, 2016. "Energy and reliability optimization of a system that combines daylighting and artificial sources. A case study carried out in academic buildings," Applied Energy, Elsevier, vol. 169(C), pages 250-266.
    8. Angreine Kewo & Pinrolinvic D. K. Manembu & Per Sieverts Nielsen, 2023. "A Rigorous Standalone Literature Review of Residential Electricity Load Profiles," Energies, MDPI, vol. 16(10), pages 1-27, May.
    9. Nahid-Al-Masood, & Yan, Ruifeng & Saha, Tapan Kumar, 2015. "A new tool to estimate maximum wind power penetration level: In perspective of frequency response adequacy," Applied Energy, Elsevier, vol. 154(C), pages 209-220.
    10. Eissa, M.M., 2019. "Developing incentive demand response with commercial energy management system (CEMS) based on diffusion model, smart meters and new communication protocol," Applied Energy, Elsevier, vol. 236(C), pages 273-292.

    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. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
    2. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    3. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    4. Frayssinet, Loïc & Merlier, Lucie & Kuznik, Frédéric & Hubert, Jean-Luc & Milliez, Maya & Roux, Jean-Jacques, 2018. "Modeling the heating and cooling energy demand of urban buildings at city scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2318-2327.
    5. Huang, Yunyou & Zhan, Jianfeng & Luo, Chunjie & Wang, Lei & Wang, Nana & Zheng, Daoyi & Fan, Fanda & Ren, Rui, 2019. "An electricity consumption model for synthesizing scalable electricity load curves," Energy, Elsevier, vol. 169(C), pages 674-683.
    6. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    7. Verdejo, Humberto & Awerkin, Almendra & Becker, Cristhian & Olguin, Gabriel, 2017. "Statistic linear parametric techniques for residential electric energy demand forecasting. A review and an implementation to Chile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 512-521.
    8. 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.
    9. Boßmann, T. & Staffell, I., 2015. "The shape of future electricity demand: Exploring load curves in 2050s Germany and Britain," Energy, Elsevier, vol. 90(P2), pages 1317-1333.
    10. Seya, Hajime & Yamagata, Yoshiki & Nakamichi, Kumiko, 2016. "Creation of municipality level intensity data of electricity in Japan," Applied Energy, Elsevier, vol. 162(C), pages 1336-1344.
    11. Adeoye, Omotola & Spataru, Catalina, 2019. "Modelling and forecasting hourly electricity demand in West African countries," Applied Energy, Elsevier, vol. 242(C), pages 311-333.
    12. Nouha Dkhili & David Salas & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2021. "Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation," Energies, MDPI, vol. 14(6), pages 1-28, March.
    13. Grandjean, A. & Adnot, J. & Binet, G., 2012. "A review and an analysis of the residential electric load curve models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6539-6565.
    14. José María Flores-Arias & Lucio Ciabattoni & Andrea Monteriù & Francisco José Bellido-Outeiriño & Antonio Escribano & Emilio José Palacios-Garcia, 2018. "First Approach to a Holistic Tool for Assessing RES Investment Feasibility," Sustainability, MDPI, vol. 10(4), pages 1-34, April.
    15. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    16. Karunathilake, Hirushie & Hewage, Kasun & Sadiq, Rehan, 2018. "Opportunities and challenges in energy demand reduction for Canadian residential sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2005-2016.
    17. Yilmaz, S. & Rinaldi, A. & Patel, M.K., 2020. "DSM interactions: What is the impact of appliance energy efficiency measures on the demand response (peak load management)?," Energy Policy, Elsevier, vol. 139(C).
    18. Hayn, Marian & Bertsch, Valentin & Zander, Anne & Nickel, Stefan & Fichtner, Wolf, 2016. "The impact of electricity tariffs on residential demand side flexibility," Working Paper Series in Production and Energy 14, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    19. Sébastien Bissey & Sébastien Jacques & Jean-Charles Le Bunetel, 2017. "The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing," Energies, MDPI, vol. 10(11), pages 1-24, October.
    20. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.

    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:eee:appene:v:115:y:2014:i:c:p:384-393. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.