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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Infobótica Research Group, Ingenia, Ingho FM, IAT, Tecopysa, Satec, Smart Home Energy (SHE). http://156.35.46.38/she/. Accessed 17 Jan 2013)
Gonzalez I et al (2012) A holistic approach to efficiency energy management systems. ICSEA 2012: The seventh international conference on software engineering advances
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
Ministerio de Fomento, Gobierno de España (2013) Código Técnico de la Edificación. http://www.codigotecnico.org. Accessed 17 Jan 2013
U.S. Department of Energy (2013) EnergyPlus Weather Data. http://apps1.eere.energy.gov/buildings/energyplus/weatherdata_about.cfm Accessed 17 Jan 2013
U.S. Department of Energy (2013) EnergyPlus. http://apps1.eere.energy.gov/buildings/energyplus/. Accessed 17 Jan 2013
ASHRAE Handbook—Fundamentals: Chapter 19 (2009) Energy estimating and modeling methods. ASHRAE, Atlanta
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
Yahoo! The Hadoop distributed file system . http://storageconference.org/2010/Papers/MSST/Shvachko.pdf
The University of Texas at Dallas StormRider: Harnessing \Storm” for Social Networks. http://www.utdallas.edu/~vvk072000/Research/StormRider/tech-report.pdf
Marz N, Warren J (2013) Big Data Principles and best practices of scalable realtime data system. Manning Publications, Greenwich
González PA, Zamarreño JM (2002) A short-term temperature forecaster based on a state space neural network. Artif Intell 15:459–464
González PA, Zamarreño JM (2004) Prediction of hourly energy consumption in buildings base don a feedback artificial neural network. Energy Build 37:595–601
Ekici BB, Aksoy UT (2009) Prediction of building energy consumption by using artificial neural networks. Adv Eng Softw 40:356–362
Raúl R (1996) Neural networks. A systematic introduction. Springer, New York
Edwards RE, New J, Parker LE (2012) Predicting future hourly residential electrical consumption: a machine learning case study. Energy Build 49:591–603
Zhao H, Magoulès F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16(6):3586–3592
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Cortés, A., Téllez, A.E., Gallardo, M., Peralta, J.J. (2014). Big Data Technology to Exploit Climate Information/Consumption Models and to Predict Future Behaviours. In: González Alonso, I. (eds) International Technology Robotics Applications. Intelligent Systems, Control and Automation: Science and Engineering, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-319-02332-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-02332-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02331-1
Online ISBN: 978-3-319-02332-8
eBook Packages: EngineeringEngineering (R0)