Big Data Technology to Exploit Climate Information/Consumption Models and to Predict Future Behaviours

  • A. Cortés
  • A. E. Téllez
  • M. Gallardo
  • J. J. Peralta
Conference paper
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 70)

Abstract

This study presents a work in progress of the Smart Home Energy project (SHE), in which tests and simulations have generated a large set of energy consumption data that has been evaluated analytically to define a prediction model for energy consumption, based on automatic machine learning. The SuperDoop Lambda Arquitecture developed by Ingenia for Big Data implementation used in the SHE project allows implementing a service to do predictions massively, developing a personalized home energy knowledge model for each home. These methods and related technology can be used also for other energy consumers, like shops, offices, buildings, industries, electrical vehicles, etc.

Keywords

Big data Lambda architecture Hadoop Storm Data mining Data science Neural networks Energy consumption predictive model Energy efficiency Energy home knowledge 

Notes

Acknowledgments

This project is funded by the Ministry of Economy and Competitiveness of Spain (IPT-2011-1237-920000). We are also very grateful to all the members of the consortium (Ingenia, Satec, Ingho, Tecopysa, Cotesa, IAT, University of Oviedo).

References

  1. 1.
    Infobótica Research Group, Ingenia, Ingho FM, IAT, Tecopysa, Satec, Smart Home Energy (SHE). http://156.35.46.38/she/. Accessed 17 Jan 2013)
  2. 2.
    Gonzalez I et al (2012) A holistic approach to efficiency energy management systems. ICSEA 2012: The seventh international conference on software engineering advancesGoogle Scholar
  3. 3.
    Secretaría General Departamento de Planificación y Estudios del IDAE (2013) Proyecto SECH-SPAHOUSEC. Análisis del consumo energético del sector residencial en España. http://www.idae.es/index.php/mod.documentos/mem.descarga?file=/documentos_Informe_SPAHOUSEC_ACC_f68291a3.pdf. Accessed 17 Jan 2013
  4. 4.
    Ministerio de Fomento, Gobierno de España (2013) Código Técnico de la Edificación. http://www.codigotecnico.org. Accessed 17 Jan 2013
  5. 5.
    U.S. Department of Energy (2013) EnergyPlus Weather Data. http://apps1.eere.energy.gov/buildings/energyplus/weatherdata_about.cfm Accessed 17 Jan 2013
  6. 6.
    U.S. Department of Energy (2013) EnergyPlus. http://apps1.eere.energy.gov/buildings/energyplus/. Accessed 17 Jan 2013
  7. 7.
    ASHRAE Handbook—Fundamentals: Chapter 19 (2009) Energy estimating and modeling methods. ASHRAE, AtlantaGoogle Scholar
  8. 8.
    Dr. Yi Zhang, Institute of Energy and Sustainable Development, De Montfort University, United Kingdom. JEPlus: an EnergyPlus simulation manager for parametrics. http://www.iesd.dmu.ac.uk/~jeplus. Accessed 17 Jan 2013
  9. 9.
    Yahoo! The Hadoop distributed file system . http://storageconference.org/2010/Papers/MSST/Shvachko.pdf
  10. 10.
    The University of Texas at Dallas StormRider: Harnessing \Storm” for Social Networks. http://www.utdallas.edu/~vvk072000/Research/StormRider/tech-report.pdf
  11. 11.
    Marz N, Warren J (2013) Big Data Principles and best practices of scalable realtime data system. Manning Publications, GreenwichGoogle Scholar
  12. 12.
    González PA, Zamarreño JM (2002) A short-term temperature forecaster based on a state space neural network. Artif Intell 15:459–464Google Scholar
  13. 13.
    González PA, Zamarreño JM (2004) Prediction of hourly energy consumption in buildings base don a feedback artificial neural network. Energy Build 37:595–601Google Scholar
  14. 14.
    Ekici BB, Aksoy UT (2009) Prediction of building energy consumption by using artificial neural networks. Adv Eng Softw 40:356–362Google Scholar
  15. 15.
    Raúl R (1996) Neural networks. A systematic introduction. Springer, New YorkGoogle Scholar
  16. 16.
    Edwards RE, New J, Parker LE (2012) Predicting future hourly residential electrical consumption: a machine learning case study. Energy Build 49:591–603Google Scholar
  17. 17.
    Zhao H, Magoulès F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16(6):3586–3592Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • A. Cortés
    • 1
  • A. E. Téllez
    • 2
  • M. Gallardo
    • 2
  • J. J. Peralta
    • 3
  1. 1.Ingeniería e Integración Avanzadas (Ingenia)MálagaSpain
  2. 2.Ingho FMMálagaSpain
  3. 3.Instituto Andaluz de Tecnología (IAT)SevillaSpain

Personalised recommendations