IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i19p3712-d271653.html
   My bibliography  Save this article

Generic Ontology of Energy Consumption Households

Author

Listed:
  • Joanna Kott

    (Department of Management Infrastructure, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland)

  • Marek Kott

    (Department of Electrical Power Engineering, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland)

Abstract

The smart concept has changed both household electrical systems (smart home) and the whole electric power system (smart grid). It has facilitated much more efficient electrical energy management. Therefore, there is a need to develop a detailed model and knowledge base at the micro-system level, which can respond to changes in the electric power system. Extensive knowledge (know-how), large financial outlays, and access to modern technologies are necessary in order to design and build a functional smart grid. The first installations were made in highly developed countries. Currently, a significant proportion of newly built power installations in Europe have the features of a smart grid type. Developing countries, such as Poland, should benefit from the experience of other countries in the process of building modern installations. The article addresses the energy performance of a household and the ontology of a household micro-system, while taking into account the possibility of it being controlled via energy management systems (EMS).

Suggested Citation

  • Joanna Kott & Marek Kott, 2019. "Generic Ontology of Energy Consumption Households," Energies, MDPI, vol. 12(19), pages 1-19, September.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3712-:d:271653
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/19/3712/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/19/3712/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
    2. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
    3. Roberto S. Netto & Guilherme R. Ramalho & Benedito D. Bonatto & Otavio A. S. Carpinteiro & A. C. Zambroni de Souza & Denisson Q. Oliveira & Rodrigo A. S. Braga, 2018. "Real-Time Framework for Energy Management System of a Smart Microgrid Using Multiagent Systems," Energies, MDPI, vol. 11(3), pages 1-17, March.
    4. Claudia Zanabria & Ali Tayyebi & Filip Pröstl Andrén & Johannes Kathan & Thomas Strasser, 2017. "Engineering Support for Handling Controller Conflicts in Energy Storage Systems Applications," Energies, MDPI, vol. 10(10), pages 1-24, October.
    5. Hossein Shayeghi & Elnaz Shahryari & Mohammad Moradzadeh & Pierluigi Siano, 2019. "A Survey on Microgrid Energy Management Considering Flexible Energy Sources," Energies, MDPI, vol. 12(11), pages 1-26, June.
    6. Mayank Jha & Frede Blaabjerg & Mohammed Ali Khan & Varaha Satya Bharath Kurukuru & Ahteshamul Haque, 2019. "Intelligent Control of Converter for Electric Vehicles Charging Station," Energies, MDPI, vol. 12(12), pages 1-25, June.
    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. Marko Milojević & Paweł Nowodziński & Ivica Terzić & Svetlana Danshina, 2021. "Households’ Energy Autonomy: Risks or Benefits for a State?," Energies, MDPI, vol. 14(7), pages 1-16, April.
    2. Antonio De Nicola & Maria Luisa Villani, 2021. "Smart City Ontologies and Their Applications: A Systematic Literature Review," Sustainability, MDPI, vol. 13(10), pages 1-40, May.
    3. Gleydson de Oliveira Cavalcanti & Handson Claudio Dias Pimenta, 2023. "Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review," Energies, MDPI, vol. 16(15), pages 1-29, August.
    4. Kowalska-Pyzalska, Anna & Kott, Joanna & Kott, Marek, 2020. "Why Polish market of alternative fuel vehicles (AFVs) is the smallest in Europe? SWOT analysis of opportunities and threats," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    5. August Wierling & Valeria Jana Schwanitz & Sebnem Altinci & Maria Bałazińska & Michael J. Barber & Mehmet Efe Biresselioglu & Christopher Burger-Scheidlin & Massimo Celino & Muhittin Hakan Demir & Ric, 2021. "FAIR Metadata Standards for Low Carbon Energy Research—A Review of Practices and How to Advance," Energies, MDPI, vol. 14(20), pages 1-20, October.

    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. Sandoval, Cinda & Alvarado, Victor M. & Carmona, Jean-Claude & Lopez Lopez, Guadalupe & Gomez-Aguilar, J.F., 2017. "Energy management control strategy to improve the FC/SC dynamic behavior on hybrid electric vehicles: A frequency based distribution," Renewable Energy, Elsevier, vol. 105(C), pages 407-418.
    2. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    3. Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    4. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    5. Peng, Fei & Zhao, Yuanzhe & Li, Xiaopeng & Liu, Zhixiang & Chen, Weirong & Liu, Yang & Zhou, Donghua, 2017. "Development of master-slave energy management strategy based on fuzzy logic hysteresis state machine and differential power processing compensation for a PEMFC-LIB-SC hybrid tramway," Applied Energy, Elsevier, vol. 206(C), pages 346-363.
    6. Tharsis Teoh & Oliver Kunze & Chee-Chong Teo & Yiik Diew Wong, 2018. "Decarbonisation of Urban Freight Transport Using Electric Vehicles and Opportunity Charging," Sustainability, MDPI, vol. 10(9), pages 1-20, September.
    7. Ru-Jen Lin & Rong-Huei Chen & Thao-Minh Ho, 2013. "Market Demand, Green Innovation, and Firm Performance: Evidence from Hybrid Vehicle Industry," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    8. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    9. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    10. Shi, Xiao & Pan, Jian & Wang, Hewu & Cai, Hua, 2019. "Battery electric vehicles: What is the minimum range required?," Energy, Elsevier, vol. 166(C), pages 352-358.
    11. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    12. Ming Cai & Weijie Chen & Xiaojun Tan, 2017. "Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model," Energies, MDPI, vol. 10(10), pages 1-16, October.
    13. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    14. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    15. Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation," Energy, Elsevier, vol. 206(C).
    16. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    17. Andrzej Ożadowicz & Gabriela Walczyk, 2023. "Energy Performance and Control Strategy for Dynamic Façade with Perovskite PV Panels—Technical Analysis and Case Study," Energies, MDPI, vol. 16(9), pages 1-23, April.
    18. Menon, Ramanunni P. & Paolone, Mario & Maréchal, François, 2013. "Study of optimal design of polygeneration systems in optimal control strategies," Energy, Elsevier, vol. 55(C), pages 134-141.
    19. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    20. Zahurul, S. & Mariun, N. & Grozescu, I.V. & Tsuyoshi, Hanamoto & Mitani, Yasunori & Othman, M.L. & Hizam, H. & Abidin, I.Z., 2016. "Future strategic plan analysis for integrating distributed renewable generation to smart grid through wireless sensor network: Malaysia prospect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 978-992.

    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:jeners:v:12:y:2019:i:19:p:3712-:d:271653. 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.