Can I Shift My Load? Optimizing the Selection of the Best Electrical Tariff for Tertiary Buildings

  • Oihane Kamara-EstebanEmail author
  • Cruz E. Borges
  • Diego Casado-Mansilla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


Sustainability is strongly related to the appropriate use of available resources, being an important cornerstone in any company’s administration due to the direct influence on its efficiency and ability to compete in the global market. Therefore, the intelligent and proper management of these resources is a pressing matter in terms of cost savings. Among the possible alternatives for optimisation, the one regarding electricity consumption stands out due to its strong influence on the expenses account. In general, this type of optimisation can be carried out from two different perspectives: one that concerns the efficient use of energy itself and the other related to the proper adjustment of the electricity contract so that it meets the infrastructure needs while avoiding extra costs derived from poorly sized bills. This paper describes the application of an artificial intelligence based methodology for the optimisation of the parameters contracted in the electricity tariff in the Spanish market. This technique is able to adjust the power term needed so that the global economic cost derived from energy consumption is significantly reduced. The papers discusses the impact that this proposal may have on a demand response scenario associated to load shifting practices within university buildings. Furthermore, the role of human beings, specifically university employees, and their actions towards reducing the overuse of power consumption at the same time is also addressed.


Demand response Energy costs Forecasting Flexibility Genetic algorithms 



We acknowledge the support of the Spanish government for SentientThings project under Grant No.: TIN2017-90042-R.


  1. 1.
    Ratnatunga, J.: Carbon cost accounting: the impact of global warming on the cost accounting profession. J. Appl. Manag. Acc. Res. 5, 01 (2007)Google Scholar
  2. 2.
    Kamal, W.: Improving energy efficiency–the cost-effective way to mitigate global warming. Energy Convers. Manag. 38(1), 39–59 (1997)CrossRefGoogle Scholar
  3. 3.
    Allouhi, A., El Fouih, Y., Kousksou, T., Jamil, A., Zeraouli, Y., Mourad, Y.: Energy consumption and efficiency in buildings: current status and future trends. J. Clean. Prod. 109, 118–130 (2015)CrossRefGoogle Scholar
  4. 4.
    Vecchiato, D., Tempesta, T.: Public preferences for electricity contracts including renewable energy: a marketing analysis with choice experiments. Energy 88, 168–179 (2015)CrossRefGoogle Scholar
  5. 5.
    Zhang, J., et al.: Blockchain based intelligent distributed electrical energy systems: needs, concepts, approaches and vision. Zidonghua Xuebao/Acta Automatica Sinica 43(9), 1544–1554 (2017)Google Scholar
  6. 6.
    Zhao, S., Wang, B., Li, Y., Li, Y.: Integrated energy transaction mechanisms based on blockchain technology. Energies 11(9), 2412 (2018)CrossRefGoogle Scholar
  7. 7.
    Wain, N.: Households still baffled by energy bills and vote them the most difficult paperwork to understand despite rules to make them clearer., October 2014Google Scholar
  8. 8.
    Kuster, C., Rezgui, Y., Mourshed, M.: Electrical load forecasting models: a critical systematic review. Sustain. Cities Soc. 35, 257–270 (2017)CrossRefGoogle Scholar
  9. 9.
    Borges, C.E., Penya, Y.K., Fernández, I.: Optimal combined short-term building load forecasting. In: 2011 IEEE PES Innovative Smart Grid Technologies, pp. 1–7, November 2011Google Scholar
  10. 10.
    Raza, M., Khosravi, A.: A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sustain. Energy Rev. 50, 1352–1372 (2015)CrossRefGoogle Scholar
  11. 11.
    Abdelaziz, E., Saidur, R., Mekhilef, S.: A review on energy saving strategies in industrial sector. Renew. Sustain. Energy Rev. 15(1), 150–168 (2011)CrossRefGoogle Scholar
  12. 12.
    Casado-Mansilla, D., et al.: A human-centric context-aware IoT framework for enhancing energy efficiency in buildings of public use. IEEE Access 6, 31444–31456 (2018)CrossRefGoogle Scholar
  13. 13.
    Kessels, K., Kraan, C., Karg, L., Maggiore, S., Valkering, P., Laes, E.: Fostering residential demand response through dynamic pricing schemes: a behavioural review of smart grid pilots in Europe. Sustainability (Switzerland) 8(9), 929 (2016)CrossRefGoogle Scholar
  14. 14.
    Quintal, F., Jorge, C., Nisi, V., Nunes, N.: Watt-I-See: a tangible visualization of energy. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, ser. AVI ’16, pp. 120–127. ACM, New York, NY, USA (2016)Google Scholar
  15. 15.
    Sugarman, V., Lank, E.: Designing persuasive technology to manage peak electricity demand in ontario homes. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1975–1984. ACM (2015)Google Scholar
  16. 16.
    Mohsenian-Rad, A.-H., Wong, V., Jatskevich, J., Schober, R., Leon-Garcia, A.: Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans. Smart Grid 1(3), 320–331 (2010)CrossRefGoogle Scholar
  17. 17.
    Kreith, F., Goswami, D.Y.: Energy Management and Conservation Handbook. CRC Press, Boca Raton (2007)CrossRefGoogle Scholar
  18. 18.
    Reed, G.F., Lynn, F., Meade, B.D.: Use of coefficient of variation in assessing variability of quantitative assays. Clin. Diagn. Lab. Immunol. 9(6), 1235–1239 (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DeustoTech - Universidad de DeustoBilbaoSpain

Personalised recommendations