IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v45y2015icp318-335.html
   My bibliography  Save this article

Home energy management systems: A review of modelling and complexity

Author

Listed:
  • Beaudin, Marc
  • Zareipour, Hamidreza

Abstract

The increasing demand for electricity and the emergence of smart grids have presented new opportunities for home energy management systems (HEMS) in demand response markets. HEMS are demand response tools that shift and curtail demand to improve the energy consumption and production profile of a dwelling on behalf of a consumer. HEMS usually create optimal consumption and production schedules by considering multiple objectives such as energy costs, environmental concerns, load profiles, and consumer comfort.

Suggested Citation

  • Beaudin, Marc & Zareipour, Hamidreza, 2015. "Home energy management systems: A review of modelling and complexity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 318-335.
  • Handle: RePEc:eee:rensus:v:45:y:2015:i:c:p:318-335
    DOI: 10.1016/j.rser.2015.01.046
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032115000568
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2015.01.046?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. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
    2. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
    3. Clastres, C. & Ha Pham, T.T. & Wurtz, F. & Bacha, S., 2010. "Ancillary services and optimal household energy management with photovoltaic production," Energy, Elsevier, vol. 35(1), pages 55-64.
    4. Matallanas, E. & Castillo-Cagigal, M. & Gutiérrez, A. & Monasterio-Huelin, F. & Caamaño-Martín, E. & Masa, D. & Jiménez-Leube, J., 2012. "Neural network controller for Active Demand-Side Management with PV energy in the residential sector," Applied Energy, Elsevier, vol. 91(1), pages 90-97.
    5. Lujano-Rojas, Juan M. & Monteiro, Cláudio & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimum residential load management strategy for real time pricing (RTP) demand response programs," Energy Policy, Elsevier, vol. 45(C), pages 671-679.
    6. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    7. Bagge, Hans & Johansson, Dennis, 2011. "Measurements of household electricity and domestic hot water use in dwellings and the effect of different monitoring time resolution," Energy, Elsevier, vol. 36(5), pages 2943-2951.
    8. Stephen P. Holland & Erin T. Mansur, 2008. "Is Real-Time Pricing Green? The Environmental Impacts of Electricity Demand Variance," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 550-561, August.
    9. 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.
    10. Naveed Ul Hassan & Muhammad Adeel Pasha & Chau Yuen & Shisheng Huang & Xiumin Wang, 2013. "Impact of Scheduling Flexibility on Demand Profile Flatness and User Inconvenience in Residential Smart Grid System," Energies, MDPI, vol. 6(12), pages 1-28, December.
    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. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    2. Feuerriegel, Stefan & Neumann, Dirk, 2016. "Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications," Energy Policy, Elsevier, vol. 96(C), pages 231-240.
    3. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    4. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    5. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    6. Sarker, Eity & Seyedmahmoudian, Mehdi & Jamei, Elmira & Horan, Ben & Stojcevski, Alex, 2020. "Optimal management of home loads with renewable energy integration and demand response strategy," Energy, Elsevier, vol. 210(C).
    7. Nezamoddini, Nasim & Wang, Yong, 2017. "Real-time electricity pricing for industrial customers: Survey and case studies in the United States," Applied Energy, Elsevier, vol. 195(C), pages 1023-1037.
    8. Cortés-Arcos, Tomás & Bernal-Agustín, José L. & Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Contreras, Javier, 2017. "Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology," Energy, Elsevier, vol. 138(C), pages 19-31.
    9. Benedict J. Drasch & Gilbert Fridgen & Lukas Häfner, 2020. "Demand response through automated air conditioning in commercial buildings—a data-driven approach," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 1491-1525, November.
    10. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
    11. Xu, Bing & Nayak, Amar & Gray, David & Ouenniche, Jamal, 2016. "Assessing energy business cases implemented in the North Sea Region and strategy recommendations," Applied Energy, Elsevier, vol. 172(C), pages 360-371.
    12. Kamalanathan Ganesan & João Tomé Saraiva & Ricardo J. Bessa, 2019. "On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs," Energies, MDPI, vol. 12(14), pages 1-20, July.
    13. Fischer, David & Madani, Hatef, 2017. "On heat pumps in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 342-357.
    14. Elma, Onur & Taşcıkaraoğlu, Akın & Tahir İnce, A. & Selamoğulları, Uğur S., 2017. "Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts," Energy, Elsevier, vol. 134(C), pages 206-220.
    15. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    16. Feuerriegel, Stefan & Neumann, Dirk, 2014. "Measuring the financial impact of demand response for electricity retailers," Energy Policy, Elsevier, vol. 65(C), pages 359-368.
    17. 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.
    18. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    19. Thibaut Th'eate & Antonio Sutera & Damien Ernst, 2023. "Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis," Papers 2301.11587, arXiv.org.
    20. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.

    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:rensus:v:45:y:2015:i:c:p:318-335. 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/600126/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.