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On-demand energy monitoring and response architecture in a ubiquitous world

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Abstract

Energy demand is increasing globally, and in consequence greenhouse-gas emissions from this sector are on the rise as well. This trend is set to continue, driven primarily by the economic growth and the rising population. Solutions in this area go hand in hand with the worldwide deployment of policies that look forward a better management and usage of energy in both domestic and industrial scopes. In this line, load balancing through demand-response strategies comes out as one of the most effective and immediate actions aimed at achieving efficiency in the use of energy resources. We present GeoWorldSim, an agent-based simulation platform that integrates the development of a human activity model as well as the communication middleware known as FI-WARE in order to test the best communication architectures available for the implementation of demand-response strategies.

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References

  1. Adelantado F, Vilajosana X, Tuset-Peiró P, Martínez B, Melià J (2016) Understanding the limits of lorawan. CoRR arXiv:abs/1607.08011

  2. Agency EE (2015) Air quality in europe 2015 report. EEA, Report 5

  3. Agency IE (2015) Energy climate and change. World Energy Outlook Special Report

  4. Al-Bahadili H (2012) Simulation in computer network design and modeling: use and analysis. IGI Global

  5. Albadi MH, El-Saadany E (2008) A summary of demand response in electricity markets. Electr Power Syst Res 78(11):1989–1996

    Article  Google Scholar 

  6. Allcott H, Mullainathan S (2010) Behavior and energy policy. Science 327(5970):1204–1205

    Article  Google Scholar 

  7. Apostolou G, Krinidis S, Ioannidis D, Tzovaras D, Borges CE, Casado-Mansilla D, de Ipiña DL (2016) Greensoul; a novel platform for the reduction of energy consumption in communal and shared spaces. In: 2016 4th International symposium on environmental friendly energies and applications (EFEA), pp 1–6. doi:10.1109/EFEA.2016.7748783

  8. Apostolou G, Krinidis S, Ioannidis D, Tzovaras D, Borges CE, Casado-Mansilla D, Lopez-de Ipina D (2016) Eco-aware persuasive networked data devices for user engagement in energy efficiency: the greensoul concept. In: Proceedings of the 4th international symposium on environment-friendly energies and applications

  9. Azkune G, Almeida A, López-de Ipiña D, Chen L (2015) Combining users’ activity survey and simulators to evaluate human activity recognition systems. Sensors 15(4):8192–8213. doi:10.3390/s150408192

    Article  Google Scholar 

  10. Benjamini Y (1988) Opening the box of a boxplot. Am Stat 42(4):257–262

    Google Scholar 

  11. Chen L, Hoey J, Nugent C, Cook D, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C 42(6):790–808. doi:10.1109/TSMCC.2012.2198883

    Article  Google Scholar 

  12. Commision E (2015) A framework strategy for a resilient energy union with a forward-looking climate change policy. Communication from the Commission to the European Parliament, the Council and the Committee of the Regions

  13. Consortium FW (2012−2016) FI-WARE architecture. http://cordis.europa.eu/fp7/ict/netinnovation/deliverables/fi-ware/fi-ware-d231b.pdf

  14. Cook DJ, Youngblood M, Heierman III EO, Gopalratnam K, Rao S, Litvin A, Khawaja F (2003) MavHome: an agent-based smart home. In: Null, IEEE, p 521

  15. Education BG (2015−2016) I2Basque Network. http://www.i2basque.es/

  16. Espinoza A, Penya Y, Nieves JC, Ortega M, Pea A, Rodrguez D (2013) Supporting business workflows in smart grids: an intelligent nodes-based approach. IEEE Trans Ind Inform 9(3):1384–1397. doi:10.1109/TII.2013.2256792

    Article  Google Scholar 

  17. FI-WARE (2015−2016) Cloud FI-WARE. https://account.lab.fiware.org/

  18. Fornié MAS, Sainz-Maza RG (2013) Desarrollos de smart grids para el fomento de la eficiencia energética. In: Anales de mecánica y electricidad, Asociacion de Ingenieros del ICAI, vol 90, pp 62–67

  19. Helal S, Mann W, El-Zabadani H, King J, Kaddoura Y, Jansen E (2005) The gator tech smart house: a programmable pervasive space. Computer 38(3):50–60. doi:10.1109/MC.2005.107

    Article  Google Scholar 

  20. Higuchi N, Xu Y, Yamamoto T, Nara K (2014) Functions and operational experiences of real scale smart grid experimental system. In: 2014 IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA), IEEE, pp 624–629

  21. Kamara-Esteban O, GAzkune, Pijoan A, Borges CE, de Ipiña DL (2016) MASSHA: an agent-based approach for human activity simulation in intelligent environments, submitted

  22. Kamara-Esteban O, Sorrosal G, Pijoan A, Castillo-Calzadilla T, Iriarte-Lopez X, Macarulla-Arenaza AM, Martin C, Alonso-Vicario A, Borges CE (2016) Bridging the gap between real and simulated environments: an agent-based hybrid smart home simulator for complex systems. In: Proceedings of the Intl IEEE conferences on ubiquitous intelligence & computing, pp 220–227

  23. Karnouskos S, De Holanda TN, (2009) Simulation of a smart grid city with software agents. In: Computer modeling and simulation, 2009. EMS’09. Third UKSim European symposium on, IEEE, pp 424–429

  24. Kunsch HR (1989) The jackknife and the bootstrap for general stationary observations. Ann Stat pp 1217–1241

  25. Lesser V, Atighetchi M, Benyo B, Horling B, Raja A, Vincent R, Wagner T, Xuan P, Zhang SX (1999) The UMASS intelligent home project. In: Proceedings of the third annual conference on autonomous agents, Seattle, Washington, USA. ACM, New York, USA, pp 291–298

  26. Lopez-de Armentia J, Casado-Mansilla D, Lopez-de Ipina D (2013) Saving energy through collaborative eco-aware everyday things. In: The seventh international conference on innovative mobile and internet services in ubiquitous computing, IEEE Xplore, Taichung, Taiwan, pp 489–493. doi:10.1109/IMIS.2013.88

  27. Oliveira P, Pinto T, Morais H, Vale Z (2012) Masgrip: A multi-agent smart grid simulation platform. In: Power and energy society general meeting, 2012 IEEE, pp 1–8. doi:10.1109/PESGM.2012.6345649

  28. Penya YK, Nieves JC, Espinoza A, Borges CE, Peña A, Ortega M (2012) Distributed semantic architecture for smart grids. Energies 5(11):4824–4843

    Article  Google Scholar 

  29. Penya YK, Borges CE, Pea A, Esteban OK (2013) Service-orientation vs. real-time: Integrating smart-homes into the smart-grid. In: Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE, pp 5755–5760. doi:10.1109/IECON.2013.6700077

  30. Pijoan A, Kamara-Esteban O, Borges CE (2015) Environment modelling for spatial load forecasting. In: Weyns D, Michel F (eds) Agent environments for multi-agent systems IV, lecture notes in computer science, vol 9068, Springer, Berlin, pp 188–206. doi:10.1007/978-3-319-23850-0_12

  31. Pohlert T (2014) The pairwise multiple comparison of mean ranks package (pmcmr). R package

  32. Project Q (1995−2016) Qt \(\vert\) Cross-platform application & UI development framework. http://www.qt.io/

  33. Response SS (2016) IoT devices being increasingly used for DDoS attacks. https://www.symantec.com/connect/blogs/iot-devices-being-increasingly-used-ddos-attacks

  34. Ricci A, Rodriguez-Aguilar J, Pijoan A, Zambonelli F (2015) Mixed environments for mas: Bringing humans in the loop. In: Weyns D, Michel F (eds) Agent environments for multi-agent systems IV, lecture notes in computer science, vol 9068, Springer, Berlin, pp 52–60. doi:10.1007/978-3-319-23850-0_4

  35. Skala K, Davidovic D, Afgan E, Sovic I, Sojat Z (2015) Scalable distributed computing hierarchy: Cloud, fog and dew computing. Open J Cloud Comput 2(1):16–24. doi:10.19210/1002.2.1.16

    Google Scholar 

  36. Van Nguyen T, Kim JG, Choi D (2009) ISS: the interactive smart home simulator. In: Advanced communication technology, 2009. ICACT 2009. 11th International conference on, IEEE, vol 3, pp 1828–1833

  37. Wooldridge M, Jennings NR (1995) Intelligent agents: theory and practice. Knowl Eng Rev 10:115–152. doi:10.1017/S0269888900008122

    Article  Google Scholar 

  38. Xia F, Yang LT, Wang L, Vinel A (2012) Internet of things. Int J Commun Syst 25(9):1101

    Article  Google Scholar 

  39. Zdravković M, Trajanović M, Sarraipa J, Jardim-Gonçalves R, Lezoche M, Aubry A, Panetto H (2016) Survey of Internet-of-Things platforms. In: 6th International Conference on Information Society and Techology, ICIST 2016, Kopaonik, Serbia, vol 1, pp 216–220, https://hal.archives-ouvertes.fr/hal-01298141, ISBN:978-86-85525-18-6

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Acknowledgements

This work was carried out with the financial support of (a) the European Union’s Horizon 2020 Research and Innovation Programme Under Grant Agreement No. 696129, given to GREENSOUL project, (b) Industrial Ph.D. Grant given by the University of Deusto (2015–2018), and (c) Ph.D. Grant PRE_2015_2_0003 given by the Basque Government.

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Authors and Affiliations

  1. DeustoTech - Fundación Deusto, Avda. Universidades, 24, 48007, Bilbao, Spain

    Oihane Kamara-Esteban, Ander Pijoan, Ainhoa Alonso-Vicario & Cruz E. Borges

  2. Facultad Ingeniería, Universidad de Deusto, Avda. Universidades, 24, 48007, Bilbao, Spain

    Oihane Kamara-Esteban, Ander Pijoan, Ainhoa Alonso-Vicario & Cruz E. Borges

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  1. Oihane Kamara-Esteban
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  2. Ander Pijoan
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  3. Ainhoa Alonso-Vicario
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  4. Cruz E. Borges
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Correspondence to Oihane Kamara-Esteban.

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Kamara-Esteban, O., Pijoan, A., Alonso-Vicario, A. et al. On-demand energy monitoring and response architecture in a ubiquitous world. Pers Ubiquit Comput 21, 537–551 (2017). https://doi.org/10.1007/s00779-017-1014-4

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  • DOI: https://doi.org/10.1007/s00779-017-1014-4

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