IDEAS home Printed from
   My bibliography  Save this article

Multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling


  • Pamulapati, Trinadh
  • Mallipeddi, Rammohan
  • Lee, Minho


Residential consumers desire to minimize electricity bills while maximizing comfort by appropriate appliance scheduling. The conflicting nature of the objectives facilitates a multi-objective formulation that can provide a set of trade-off schedules enabling better decision making. In literature, user preference or comfort regarding each device at each time instance is obtained explicitly. In addition, scheduling interval of 1-hour is considered because reducing scheduling interval to 1 or 5 min drastically increases – (1) the dimensionality of search space and complicates the search process, and (2) the number of time instances for which the user has to explicitly provide the preference resulting in human fatigue. However, it is essential to schedule the devices at lower scheduling intervals to precisely-estimate the electricity consumption due to the presence of high power devices such as microwave that operate for shorter intervals (<5 min). In this work, we employ an efficient and scalable multi-objective evolutionary algorithm to solve the scheduling problem. In addition, the user preference is implicitly estimated from the past usage patterns obtained using energy disaggregation. And, if the estimated user preference deviates from the user expectation then the user can modify preference using weights referred to as priority weights. The novel implicit user satisfaction modeling and interactive customization through priority weights makes the proposed work a standalone approach suitable for any user. Experimental results and analysis for various user priorities and scheduling intervals (ranging from 1-minute to 1-hour) proves that the proposed framework is able to provide generalized schedules.

Suggested Citation

  • Pamulapati, Trinadh & Mallipeddi, Rammohan & Lee, Minho, 2020. "Multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling," Applied Energy, Elsevier, vol. 267(C).
  • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920302026
    DOI: 10.1016/j.apenergy.2020.114690

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

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

    References listed on IDEAS

    1. Shahzad, Sally & Brennan, John & Theodossopoulos, Dimitris & Hughes, Ben & Calautit, John Kaiser, 2017. "Energy and comfort in contemporary open plan and traditional personal offices," Applied Energy, Elsevier, vol. 185(P2), pages 1542-1555.
    2. Campillo, Javier & Dahlquist, Erik & Wallin, Fredrik & Vassileva, Iana, 2016. "Is real-time electricity pricing suitable for residential users without demand-side management?," Energy, Elsevier, vol. 109(C), pages 310-325.
    3. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    4. Karmellos, M. & Kiprakis, A. & Mavrotas, G., 2015. "A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies," Applied Energy, Elsevier, vol. 139(C), pages 131-150.
    5. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    6. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    7. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    8. Soares, Ana & Antunes, Carlos Henggeler & Oliveira, Carlos & Gomes, Álvaro, 2014. "A multi-objective genetic approach to domestic load scheduling in an energy management system," Energy, Elsevier, vol. 77(C), pages 144-152.
    9. Zhou, Yang & Shi, Zhixiong & Shi, Zhengyu & Gao, Qing & Wu, Libo, 2019. "Disaggregating power consumption of commercial buildings based on the finite mixture model," Applied Energy, Elsevier, vol. 243(C), pages 35-46.
    10. Kim, Jimin & Hong, Taehoon & Jeong, Jaemin & Koo, Choongwan & Jeong, Kwangbok, 2016. "An optimization model for selecting the optimal green systems by considering the thermal comfort and energy consumption," Applied Energy, Elsevier, vol. 169(C), pages 682-695.
    Full references (including those not matched with items on IDEAS)


    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:267:y:2020:i:c:s0306261920302026. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.