Dynamic Energy Management Method with Demand Response Interaction Applied in an Office Building

  • Filipe Fernandes
  • Luis Gomes
  • Hugo Morais
  • Marco Silva
  • Zita Vale
  • Juan M. Corchado
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 473)


The intelligent management systems of the end consumers are endowed with advanced functions being one of them the interaction with external entities through the automatic participation in demand response programs. The development of the intelligent management systems is to reduce the energy consumption based on internal information and on the interaction with an external entity. Moreover, the management approaches results in an active participation of the consumers in the operation of the smart grids and microgrids concepts. The paper developed presents the application of a dynamic priority method in SCADA Office Intelligent Context Awareness Management system to manage the energy resources installed in an office building. The intelligent management method allows the dynamic active participation of the office building in the DR events considering the real data of consumption and generation of one building in Polytechnic of Porto. The main goal of the methodology is to obtain a dynamic scheduling for all energy resources with little interference in the comfort of users. The results of dynamic management model in office building are discussed for the participation in 8 hours demand response event. The power limit of the scenario depends on the consumption and micro-generation power of an October day.


Demand Response Dynamic priority method Energy management Office building Energy resources 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kok, K., Karnouskos, S., Nestle, D., Dimeas, A., Weidlich, A., Warmer, C., Strauss, P., Buchholz, B., Drenkard, S., Hatziargyriou, N., Lioliou, V.: Smart houses for a smart grid. In: 20th International Conference on Electricity Distribution, CIRED 2009 (2009)Google Scholar
  2. 2.
    Hammerschmidt, T., Gaul, A., Schneider, J.: Smart grids are the efficient base for future energy applications. In: CIRED Workshop 2010: Sustainable Distribution Asset Management & Financing, June 2010Google Scholar
  3. 3.
    Kroposki, B., Lasseter, R., Ise, T., Morozumi, S., Papathanassiou, S., Hatziargyriou, N.: Making microgrids work. IEEE Power Energy Mag. 6(3), 40–53 (2008)CrossRefGoogle Scholar
  4. 4.
    Si, Y., Kim, J.T., Choi, I.Y., Cho, S.H.: Energy consumption characteristics of high-rise apartment buildings according to building shape and mixed-use development. Energy Build. 46, 123–131 (2012)CrossRefGoogle Scholar
  5. 5.
    Das, S.K., Cook, D.J., Battacharya, A., Heierman, E.O.: The role of prediction algorithms in the MavHome smart home architecture. IEEE Wirel. Commun. 9(6), 77–84 (2002)CrossRefGoogle Scholar
  6. 6.
    Golzar, M.G., Tajozzakerin, H.: A new intelligent remote control system for home automation and reduce energy consumption. In: 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, pp. 174–180 (2010)Google Scholar
  7. 7.
    Jiang, L., Liu, D.-Y., Yang, B.: Smart home research. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 04EX826), vol. 2, pp. 659–663 (2004)Google Scholar
  8. 8.
    Pipattanasomporn, M., Kuzlu, M., Rahman, S.: An Algorithm for Intelligent Home Energy Management and Demand Response Analysis. IEEE Trans. Smart Grid 3(4), 2166–2173 (2012)CrossRefGoogle Scholar
  9. 9.
    Figueiredo, J., Martins, J.: Energy Production System Management - Renewable energy power supply integration with Building Automation System. Energy Convers. Manag. 51(6), 1120–1126 (2010)CrossRefGoogle Scholar
  10. 10.
    Faria, P., Vale, Z.: Demand response in electrical energy supply: An optimal real time pricing approach. Energy 36(8), 5374–5384 (2011)CrossRefGoogle Scholar
  11. 11.
    Fei, Y., Jiang, B.: Dynamic Residential Demand Response and Distributed Generation Management in Smart Microgrid with Hierarchical Agents. Energy Procedia 12, 76–90 (2011)CrossRefGoogle Scholar
  12. 12.
    Ye, J., Xie, Q., Xiahou, Y., Wang, C.: The research of an adaptive smart home system. In: 2012 7th International Conference on Computer Science & Education (ICCSE), pp. 882–887 (2012)Google Scholar
  13. 13.
    Roy, N., Roy, A., Das, S.K.: Context-aware resource management in multi-inhabitant smart homes: a nash h-learning based approach. In: Fourth Annual IEEE International Conference on Pervasive Computing and Communications (PERCOM 2006), pp. 148–158 (2006)Google Scholar
  14. 14.
    Cook, D., Das, S.: Smart Environments: Technology, Protocols and Applications. Wiley Series on Parallel and Distributed Computing (2004)Google Scholar
  15. 15.
    Fernandes, F., Morais, H., Vale, Z., Ramos, C.: Dynamic load management in a smart home to participate in demand response events. Energy Build. 82, 592–606 (2014)CrossRefGoogle Scholar
  16. 16.
    Gomes, L., Faria, P., Fernandes, F., Vale, Z., Ramos, C.: Domestic consumption simulation and management using a continuous consumption management and optimization algorithm. In: Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference (2014)Google Scholar
  17. 17.
    Fernandes, F., Morais, H., Garcia, V.V., Gomes, L., Vale, Z., Kagan, N.: Dynamic loads and micro-generation method for a house management system. In: Power Systems Conference (PSC 2016). Clemson University (2016)Google Scholar
  18. 18.
    Gomes, L., Fernandes, F., Faria, P., Silva, M., Vale, Z., Ramos, C.: Contextual and environmental awareness laboratory for energy consumption management. In: Power Systems Conference (PSC 2015), pp. 1–6. Clemson University (2015)Google Scholar
  19. 19.
    Vinagre, E., Gomes, L., Vale, Z.: Electrical energy consumption forecast using external facility data. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 659–664 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Filipe Fernandes
    • 1
  • Luis Gomes
    • 1
  • Hugo Morais
    • 1
  • Marco Silva
    • 1
  • Zita Vale
    • 1
  • Juan M. Corchado
    • 2
  1. 1.GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and DevelopmentPolytechnic of Porto (ISEP/IPP)PortoPortugal
  2. 2.Department of Computer Science and AutomationUniversity of SalamancaSalamancaSpain

Personalised recommendations