IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4541-d602515.html
   My bibliography  Save this article

Upgrading BRICKS—The Context-Aware Semantic Rule-Based System for Intelligent Building Energy and Security Management

Author

Listed:
  • Gabriel Santos

    (GECAD Research Group, 4249-015 Porto, Portugal
    Institute of Engineering-Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Tiago Pinto

    (GECAD Research Group, 4249-015 Porto, Portugal
    Institute of Engineering-Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Zita Vale

    (Institute of Engineering-Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Rui Carvalho

    (GECAD Research Group, 4249-015 Porto, Portugal
    Institute of Engineering-Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Brígida Teixeira

    (GECAD Research Group, 4249-015 Porto, Portugal
    Institute of Engineering-Polytechnic of Porto, 4249-015 Porto, Portugal)

  • Carlos Ramos

    (GECAD Research Group, 4249-015 Porto, Portugal
    Institute of Engineering-Polytechnic of Porto, 4249-015 Porto, Portugal)

Abstract

Building management systems (BMSs) are being implemented broadly by industries in recent decades. However, BMSs focus on specific domains, and when installed on the same building, they lack interoperability to work on a centralized user interface. On the other hand, BMSs interoperability allows the implementation of complex rules based on multi-domain contexts. The Building’s Reasoning for Intelligent Control Knowledge-based System (BRICKS) is a context-aware semantic rule-based system for the intelligent management of buildings’ energy and security. It uses ontologies and semantic web technologies to interact with different domains, taking advantage of cross-domain knowledge to apply context-based rules. This work upgrades the previously presented version of BRICKS by including services for energy consumption and generation forecast, demand response, a configuration user interface (UI), and a dynamic building monitoring and management UI. The case study demonstrates BRICKS deployed at different aggregation levels in the authors’ laboratory building, managing a demand response event and interacting autonomously with other BRICKS instances. The results validate the correct functioning of the proposed tool, which contributes to the flexibility, efficiency, and security of building energy systems.

Suggested Citation

  • Gabriel Santos & Tiago Pinto & Zita Vale & Rui Carvalho & Brígida Teixeira & Carlos Ramos, 2021. "Upgrading BRICKS—The Context-Aware Semantic Rule-Based System for Intelligent Building Energy and Security Management," Energies, MDPI, vol. 14(15), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4541-:d:602515
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4541/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/15/4541/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    2. Saehong Park & Seunghyoung Ryu & Yohwan Choi & Jihyo Kim & Hongseok Kim, 2015. "Data-Driven Baseline Estimation of Residential Buildings for Demand Response," Energies, MDPI, vol. 8(9), pages 1-21, September.
    3. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
    4. Burmester, Daniel & Rayudu, Ramesh & Seah, Winston & Akinyele, Daniel, 2017. "A review of nanogrid topologies and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 760-775.
    5. Calvillo, C.F. & Sánchez-Miralles, A. & Villar, J., 2016. "Energy management and planning in smart cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 273-287.
    6. Ana Constantin & Artur Löwen & Ferdinanda Ponci & Dirk Müller & Antonello Monti, 2017. "Design, Implementation and Demonstration of Embedded Agents for Energy Management in Non-Residential Buildings," Energies, MDPI, vol. 10(8), pages 1-20, July.
    7. Kody T. Ponds & Ali Arefi & Ali Sayigh & Gerard Ledwich, 2018. "Aggregator of Demand Response for Renewable Integration and Customer Engagement: Strengths, Weaknesses, Opportunities, and Threats," Energies, MDPI, vol. 11(9), pages 1-20, September.
    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. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    2. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    3. Biancardi, Marta & Di Bari, Antonio & Villani, Giovanni, 2021. "R&D investment decision on smart cities: Energy sustainability and opportunity," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    4. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    5. Attour, Amel & Baudino, Marco & Krafft, Jackie & Lazaric, Nathalie, 2020. "Determinants of energy tracking application use at the city level: Evidence from France," Energy Policy, Elsevier, vol. 147(C).
    6. Bhatti, Bilal Ahmad & Broadwater, Robert, 2019. "Energy trading in the distribution system using a non-model based game theoretic approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Wang, Lu & Gu, Wei & Wu, Zhi & Qiu, Haifeng & Pan, Guangsheng, 2020. "Non-cooperative game-based multilateral contract transactions in power-heating integrated systems," Applied Energy, Elsevier, vol. 268(C).
    8. Li, Bo & Li, Xu & Su, Qingyu, 2022. "A system and game strategy for the isolated island electric-gas deeply coupled energy network," Applied Energy, Elsevier, vol. 306(PA).
    9. Ioannis Skouros & Athanasios Karlis, 2020. "A Study on the V2G Technology Incorporation in a DC Nanogrid and on the Provision of Voltage Regulation to the Power Grid," Energies, MDPI, vol. 13(10), pages 1-23, May.
    10. Behnaz Behi & Ali Baniasadi & Ali Arefi & Arian Gorjy & Philip Jennings & Almantas Pivrikas, 2020. "Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study," Energies, MDPI, vol. 13(10), pages 1-24, May.
    11. Nikolaos Efkarpidis & Andrija Goranović & Chen-Wei Yang & Martin Geidl & Ingo Herbst & Stefan Wilker & Thilo Sauter, 2022. "A Generic Framework for the Definition of Key Performance Indicators for Smart Energy Systems at Different Scales," Energies, MDPI, vol. 15(4), pages 1-30, February.
    12. Balta, Münevver Özge & Balta, Mustafa Tolga, 2022. "Development of a sustainable hydrogen city concept and initial hydrogen city projects," Energy Policy, Elsevier, vol. 166(C).
    13. Lucas V. Bellinaso & Edivan L. Carvalho & Rafael Cardoso & Leandro Michels, 2021. "Price-Response Matrices Design Methodology for Electrical Energy Management Systems Based on DC Bus Signalling," Energies, MDPI, vol. 14(6), pages 1-19, March.
    14. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    15. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    16. Wang, Yuanping & Ren, Hong & Dong, Liang & Park, Hung-Suck & Zhang, Yuepeng & Xu, Yanwei, 2019. "Smart solutions shape for sustainable low-carbon future: A review on smart cities and industrial parks in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 103-117.
    17. Houssem Rafik Al-Hana Bouchekara & Mohammad Shoaib Shahriar & Muhammad Sharjeel Javaid & Yusuf Abubakar Sha’aban & Makbul Anwari Muhammad Ramli, 2021. "Multi-Objective Optimization of a Hybrid Nanogrid/Microgrid: Application to Desert Camps in Hafr Al-Batin," Energies, MDPI, vol. 14(5), pages 1-24, February.
    18. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    19. Abderahman Rejeb & Karim Rejeb & Steven J. Simske & John G. Keogh, 2022. "Blockchain technology in the smart city: a bibliometric review," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 2875-2906, October.
    20. Seunghyoung Ryu & Jaekoo Noh & Hongseok Kim, 2016. "Deep Neural Network Based Demand Side Short Term Load Forecasting," Energies, MDPI, vol. 10(1), pages 1-20, December.

    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:14:y:2021:i:15:p:4541-:d:602515. 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.