A Data-Driven Methodology for Heating Optimization in Smart Buildings

Victoria Moreno, José Antonio Ferrer, José Alberto Díaz, Domingo Bravo, Victor Chang


In the paradigm of Internet of Things new applications that leverage ubiquitous connectivity enable - together with Big Data Analytics - the emergence of Smart City initiatives. This paper proposes to build a closed loop data modeling methodology in order to optimize energy consumption in a fundamental smart city scenario: smart buildings. This methodology is based on the fusion of information about relevant parameters affecting energy consumption in buildings, and the application of recommended big data techniques in order to improve knowledge acquisition for better decision making and ensure energy efficiency. Experiments carried out in different buildings demonstrate the suitability of the proposed methodology.


  1. (2016). SSP-ARFRISOL Project. www.arfrisol.es/arfrisol portal/.
  2. (2016). Technological Transfer Centre (TTC) of the University of Murcia. www.um.es/web/otri/contenido/ctt.
  3. (2016). Weather Underground. www.wunderground.com/.
  4. Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., and Weng, T. (2010). Occupancy-driven energy management for smart building automation. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, pages 1-6. ACM.
  5. Berglund, L. (1977). Mathematical models for predicting thermal comfort response of building occupants. In Ashrae Journal- American Society of Heating Refrigerating and Air-Conditioning Engineers, volume 19, pages 38-38. Amer Soc Heat Refrig Air-Conditioning Eng Inc 1791 Tullie Circle Ne, Atlanta, GA 30329.
  6. Berthold, M. R., Borgelt, C., Höppner, F., and Klawonn, F. (2010). Guide to intelligent data analysis: how to intelligently make sense of real data. Springer Science & Business Media.
  7. Cugola, G. and Margara, A. (2012). Processing flows of information: From data stream to complex event processing. ACM Computing Surveys (CSUR), 44(3):15.
  8. Darby, S. (2006). The effectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays, 486:2006.
  9. Fischer, C. (2008). Feedback on household electricity consumption: a tool for saving energy? Energy efficiency, 1(1):79-104.
  10. Foucquier, A., Robert, S., Suard, F., Stéphan, L., and Jay, A. (2013). State of the art in building modelling and energy performances prediction: A review. Renewable and Sustainable Energy Reviews, 23:272-288.
  11. Fu, Y., Li, Z., Zhang, H., and Xu, P. (2015). Using support vector machine to predict next day electricity load of public buildings with sub-metering devices. Procedia Engineering, 121:1016-1022.
  12. Hawarah, L., Ploix, S., and Jacomino, M. (2010). User behavior prediction in energy consumption in housing using bayesian networks. In Artificial Intelligence and Soft Computing, pages 372-379. Springer.
  13. Hejase, H. A. and Assi, A. H. (2012). Time-series regression model for prediction of mean daily global solar radiation in al-ain, uae. ISRN Renewable Energy, 2012.
  14. Hippert, H. S., Pedreira, C. E., and Souza, R. C. (2000). Combining neural networks and arima models for hourly temperature forecast. In ijcnn, page 4414. IEEE.
  15. Hyndman, R. J. and Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 26(3):1-22.
  16. Iqbal, R., Doctor, F., More, B., Mahmud, S., and Yousuf, U. (2016). Big data analytics: Computational intelligence techniques and application areas. Int. J. Inf. Manage, pages 10-15.
  17. Kalogirou, S. A. (2000). Applications of artificial neuralnetworks for energy systems. Applied Energy, 67(1):17-35.
  18. Kuhn, M. (2008). Caret package. Journal of Statistical Software, 28(5).
  19. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., and Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2):21.
  20. Leith, D. J., Heidl, M., and Ringwood, J. V. (2004). Gaussian process prior models for electrical load forecasting. Probabilistic Methods Applied to Power Systems, pages 112-117.
  21. Liu, H. and Motoda, H. (2012). Feature selection for knowledge discovery and data mining, volume 454. Springer Science & Business Media.
  22. Moreno, V., Zamora, M. A., and Skarmeta, A. F. (2016). A low-cost indoor localization system for energy sustainability in smart buildings. IEEE Sensors Journal, 16(9):3246-3262.
  23. Neto, A. H. and Fiorelli, F. A. S. (2008). Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and Buildings, 40(12):2169-2176.
  24. Palomares-Salas, J., De la Rosa, J., Ramiro, J., Melgar, J., Aguera, A., and Moreno, A. (2009). Arima vs. neural networks for wind speed forecasting. In Computational Intelligence for Measurement Systems and Applications, 2009. CIMSA'09. IEEE International Conference on, pages 129-133. IEEE.
  25. Provoost, R. (2013). Smart cities: innovation in energy will drive sustainable cities. [Online; Retrieved 28- 03-2016].
  26. Robert H. Shumway, D. S. S. (2010). Time Series Analysis and Its Applications With R Examples. Springer Texts in Statistics. Springer, 2nd ed. edition.
  27. Shamsnia, S. A., Shahidi, N., Liaghat, A., Sarraf, A., and Vahdat, S. F. (2011). Modeling of weather parameters using stochastic methods (arima model)(case study: Abadeh region, iran). In International Conference on Environment and Industrial Innovation. IPCBEE, volume 12.
  28. Willighagen, E. (2005). Genalg: R based genetic algorithm. R package version 0.1, 1.
  29. Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pages 29-39. Citeseer.
  30. Wortmann, F., Flüchter, K., et al. (2015). Internet of things. Business & Information Systems Engineering, 57(3):221-224.
  31. Zhao, H.-x. and Magoulès, F. (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6):3586-3592.

Paper Citation

in Harvard Style

Moreno V., Ferrer J., Díaz J., Bravo D. and Chang V. (2017). A Data-Driven Methodology for Heating Optimization in Smart Buildings . In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-245-5, pages 19-29. DOI: 10.5220/0006231200190029

in Bibtex Style

author={Victoria Moreno and José Antonio Ferrer and José Alberto Díaz and Domingo Bravo and Victor Chang},
title={A Data-Driven Methodology for Heating Optimization in Smart Buildings},
booktitle={Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - A Data-Driven Methodology for Heating Optimization in Smart Buildings
SN - 978-989-758-245-5
AU - Moreno V.
AU - Ferrer J.
AU - Díaz J.
AU - Bravo D.
AU - Chang V.
PY - 2017
SP - 19
EP - 29
DO - 10.5220/0006231200190029