Comparative Study of Artificial Neural Network Models for Forecasting the Indoor Temperature in Smart Buildings

  • Sadi AlawadiEmail author
  • David Mera
  • Manuel Fernández-Delgado
  • José A. Taboada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10268)


The implementation of efficient building energy management plans is key to the road-map of the European Union for reducing the effects of the climate change. Firstly, accurate models of the currently energy systems need to be developed. In particular, simulations of Heating, Ventilation and Air Conditioning (HVAC) systems are essential since they have a relevant impact in both energy consumption and building comfort. This paper presents a comparative of four different machine learning approaches, based on Artificial Neural Networks (ANNs), for modeling an HVAC system. The developed models have been tuned to forecast three consecutive hours of the indoor temperature of a public research building. Tests revealed that an on-line learning ANN, which is also fully trained weekly, is less affected by sensor noise and anomalies than the remaining approaches. Moreover, it can be also automatically adapted to deal with specific environmental conditions.


Smart buildings Time series prediction Energy efficiency Neural network 


  1. 1.
    Europese Commissie: A roadmap for moving to a competitive low carbon economy in 2050. Europese Commissie, Brussel (2011)Google Scholar
  2. 2.
    Zhao, H.-X., Magoulès, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16(6), 3586–3592 (2012)CrossRefGoogle Scholar
  3. 3.
    Life-OPERE. Accessed Jan 2017
  4. 4.
    Kharseh, M., Altorkmany, L., Al-Khawaj, M., Hassani, F.: Warming impact on energy use of HVAC system in buildings of different thermal qualities and in different climates. Energy Convers. Manag. 81, 106–111 (2014)CrossRefGoogle Scholar
  5. 5.
    Fong, K.F., Hanby, V.I., Chow, T.-T.: HVAC system optimization for energy management by evolutionary programming. Energy Build. 38(3), 220–231 (2006)CrossRefGoogle Scholar
  6. 6.
    Dounis, A.I., Caraiscos, C.: Advanced control systems engineering for energy and comfort management in a building environmenta review. Renew. Sustain. Energy Rev. 13(6), 1246–1261 (2009)CrossRefGoogle Scholar
  7. 7.
    Beghi, A., Cecchinato, L., Rampazzo, M., Simmini, F.: Load forecasting for the efficient energy management of HVAC systems. In: 2010 IEEE International Conference on Sustainable Energy Technologies (ICSET), pp. 1–6. IEEE (2010)Google Scholar
  8. 8.
    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
  9. 9.
    Erickson, V.L., Carreira-Perpiñán, M., Cerpa, A.E.: OBSERVE: occupancy-based system for efficient reduction of HVAC energy. In: 2011 10th International Conference on Information Processing in Sensor Networks (IPSN), pp. 258–269. IEEE (2011)Google Scholar
  10. 10.
    Rodrıguez-Mier, P., Fresquet, M., Mucientes, M., Bugarın, A.: Prediction of indoor temperatures for energy optimization in buildings (2016)Google Scholar
  11. 11.
    López, R.F., Fernandez, J.M.F.: Las redes neuronales artificiales. Netbiblo (2008)Google Scholar
  12. 12.
    Swingler, K.: Applying Neural Networks: A Practical Guide. Morgan Kaufmann, San Francisco (1996)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sadi Alawadi
    • 1
    Email author
  • David Mera
    • 1
  • Manuel Fernández-Delgado
    • 1
  • José A. Taboada
    • 1
  1. 1.Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS)Universidade de Santiago de CompostelaSantiago de CompostelaSpain

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