Decision Support System for Energy Savings and Emissions Trading in Industrial Scenarios

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 42)


This chapter proposes a decision support approach for energy savings and emissions trading based on the requirements collected through a set of industrial users. These requirements served as guideline for identification of needs that should be addressed with respect to the decision support approach, constituting a fundamental step for future platform development. The decision support approach proposes two different perspectives: support for immediate reaction (based on the paradigm of intelligent decision support implemented through the use of Case-based Reasoning together with probabilistic analysis); and support for process reconfiguration and Emission Trading System (ETS) (implemented through the use of multi-criteria decision analysis using MACBETH). The chapter illustrates a categorization approach using main criteria involved in the process and associated algorithms. Moreover the approaches proposed were successfully tested in industrial environment and the results obtained are here presented.


Decision Support Energy savings Industrial test cases 



Authors express their acknowledgement to the consortium of LifeSaver project, Context sensitive monitoring of energy consumption to support energy savings and emissions trading in industry [] funded under the 7th Research FP of the European Union (contract FP7-ICT-7-287652).


  1. 1.
    European Community: Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the Energy Performance of Buildings, L1/65, Official Journal of the European Communities, 2003Google Scholar
  2. 2.
    Kolokotsa, D., Diakaki, C., Grigoroudis, E., Stavrakakis, G., Kalaitzakis, K.: Decision support methodologies on the energy efficiency and energy management in buildings. Adv. Build. Energy Res. 3, 121–146 (2009)CrossRefGoogle Scholar
  3. 3.
    Rutman, E., Inard, C., Bailly, A., Allard, F.: A global approach of indoor environment in an air-conditioned office room. Build. Environ. 40(1), 29–37 (2005)CrossRefGoogle Scholar
  4. 4.
    Martinaitis, V., Rogoz \(\vee \) a A., Bikmaniene, I.: Criterion to evaluate the “twofold benefit” of the renovation of buildings and their elements. Energy Build. 36(1), 3–8 (2004)Google Scholar
  5. 5.
    Martinaitis, V., Kazakevicius, E., Vitkauskas, A.: A two-factor method for appraising building renovation and energy efficiency improvement projects. Energy Policy 35(1), 192–201 (2007)CrossRefGoogle Scholar
  6. 6.
    Ma, Z., Wang, S., Xu, X., Xiao, F.: A supervisory control strategy for building cooling water systems for practical and real time applications. Energy Convers. Manage. 49(8), 2324–2336 (2008)CrossRefGoogle Scholar
  7. 7.
    Kolokotsa, D.: Artificial intelligence in buildings: a review on the application of fuzzy logic. Adv. Build. Energy Res. 1, 29–54 (2007)CrossRefGoogle Scholar
  8. 8.
    Kaklauskas, A., Zavadskas, E.K., Raslanas, S.: Multivariant design and multiple criteria analysis of building Refurbishments. Energy Build. 37(4), 361–372 (2005)CrossRefGoogle Scholar
  9. 9.
    Doukas, H., Patlitzianas, K.D., Iatropoulos, K., Psarras, J.: Intelligent building energy management system using rule sets. Build. Environ. 42(10), 3562–3569 (2007)CrossRefGoogle Scholar
  10. 10.
    Chen, Z., Clements-Croome, D., Hong, J., Li, H., Xu, Q.: A multi-criteria lifespan energy efficiency approach to intelligent building assessment. Energy Build. 38(5), 393–409 (2006)CrossRefGoogle Scholar
  11. 11.
    Wong, J.K.W., Li, H.: Application of the analytic hierarchy process (AHP) in multi-criteria analysis of the selection of intelligent building systems. Build. Environ. 43(1), 108–125 (2008)CrossRefGoogle Scholar
  12. 12.
    Campos, A., Neves-Silva, R.: Decision on the best retrofit scenario to maximize energy efficiency in a building, IDT—Smart Innovation Syst. Technol. 10(Part IV), 853–862 (2011)Google Scholar
  13. 13.
    De Benedetto, L., Klemeš, J.: The environmental performance strategy map: an integrated LCA approach to support the strategic decision-making process. J. Clean. Prod. 17(10), 900–906 (2009)CrossRefGoogle Scholar
  14. 14.
    Khalili, N., Duecker, S.: Application of multi-criteria decision analysis in design of sustainable environmental management system framework. J. Clean. Prod. 47, 188–198 (2013)CrossRefGoogle Scholar
  15. 15.
    Moya, J., Pardo, N.: The potential for improvements in energy efficiency and CO\(_2\) emissions in the EU27 iron and steel industry under different payback periods. J. Clean. Prod. 52, 71–83 (2013)CrossRefGoogle Scholar
  16. 16.
    LifeSaver Consortium: LifeSaver Concept, LifeSaver project: Context sensitive monitoring of energy consumption to support energy savings and emissions trading in industry, Gran Agreement no: FP7-ICT-2011-7-287652, 2012Google Scholar
  17. 17.
    Marques, M., Neves-Silva, R.: Approach for decision support for energy savings and emissions trading based on industrial requirements. IDT—Front. Artif. Intell. Appl. 255, 74–83 (2013)Google Scholar
  18. 18.
    Marques, M., Neves-Silva, R.: Risk Based decision support system for life cycle management of industrial plants. In: Proceedings of IEEE 9th International Conference on Industrial Informatics, Caparica, Portugal (2011)Google Scholar
  19. 19.
    Bana e Costa, C., Vansnick, J., : General overview of the MACBETH approach. In: Pardalos, P.S. (ed.) Advances in Multicriteria Analysis, pp. 93–100. Kluwer Academic Publishers, Dordrecht (1995)Google Scholar
  20. 20.
    Bana e Costa, C., Vansnick, J.: MACBETH—an interactive path towards the construction of cardinal value functions. Int. Trans. Oper. Res. 1(4), 489–500 (1994)Google Scholar
  21. 21.
    Bana e Costa, C.A., De Corte, J.-M., Vansnick, J.-C.: On The Mathematical Foundations of Macbeth, The London School of Economics and Political Science, ISBN 0-7530-1525-0, 2004Google Scholar
  22. 22.
    Harris, J., Anderson, J., Shafron, W.: Investment in energy efficiency: a survey of Australian firms. Energy Policy. 28(12), 867–876 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.UNINOVA-FCT CampusCaparicaPortugal
  2. 2.FCT-UNL-FCT CampusCaparicaPortugal

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