An integrated platform for smart energy management: The CCSEM project

Keywords: Smart cities, cloud computing, energy efficiency, IoT, 5G


Energy management focuses on improving the efficient use of resources and increasing energy access in a path towards a more sustainable society. In spite of the strategies that have been proposed to guarantee increased access to the energy resources at affordable costs, there are still challenges to ensure the conservation of the resources and the protection of the environment. In line with these objectives, Cloud Computing for Smart Energy Management project (CC-SEM) is a research effort for building an integrated platform for smart monitoring, controlling, and planning energy consumption and generation in urban scenarios. CC-SEM includes, firstly, the design of a low-cost IoT device capable of monitoring, operating, and controlling home appliances according to predefined rules. It was developed with the aim of automatically manage consumption. Secondly, an analysis of 5G Narrowband IoT as a suitable cellular technology for Smart Grid outage restoration and management message delivery was addressed. Thirdly, an analysis of domestic consumption patterns was carried out to help to predict home consumption, using literature measurements. Fourthly, within the context of electrical network simulation, a forecasting and performance evaluation methodology for the generation of individual photovoltaic systems is proposed. In summary, CC-SEM presents the research efforts to provide a set of tools for controlling home devices, planning/simulating scenarios of energy generation, and to propose advances in the communication infrastructure for transmitting the generated data.

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Author Biographies

Emmanuel Luján, Universidad de Buenos Aires

Centro de Simulación Computacional para Aplicaciones Tecnológicas, CONICET.

Alejandro Otero, Universidad de Buenos Aires

Centro de Simulación Computacional para Aplicaciones Tecnológicas, CONICET.

Sebastián Valenzuela, Universidad de la República

Centro de Simulación Computacional para Aplicaciones Tecnológicas, CONICET.

Esteban Mocskos, Universidad de Buenos Aires


Luiz Angelo Steffenel, Université de Reims

Associate Professor HDR in Computer Sciences

Sergio Enrique Nesmachnow-Cánovas, Universidad de la República

Full Professor


S. A. Roosa, S. Doty, and W. C. Turner, Energy management handbook, 9th ed. The Fairmont Press, Sep. 2018.

A. Towsend, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, 1st ed. W. W. Norton & Company, Oct. 2013.

A. Soares, C. Antunes, C. Oliveira, and A. Gomes, “A multi-objective genetic approach to domestic load scheduling in an energy management system,” Energy, vol. 77, pp. 144–152, 2014.

A. Zakariazadeh, S. Jadid, and P. Siano, “Economicenvironmental energy and reserve scheduling of smart distribution systems: A multiobjective mathematical programming approach,” EnergConversManage, vol. 78, pp. 151– 164, Feb. 2014.

Y. Wahyuddin, “To what extent the grand lyon metropole can harness the smart meter project towards the governance of territorial climate energy plan (PCET) study case: Smart electric lyon project initiated by EDF [french electric utility company],” in Proc of the International Conference on Public Policy, 2017.

S. Gupta, M. Reynolds, and S. Patel, “Electrisense: Singlepoint sensing using EMI for electrical event detection and classification in the home,” in Proc of the 12th ACM Int Conf on Ubiquitous Computing. New York, NY, USA: ACM, 2010, pp. 139–148.

A. Spagnolli, N. Corradi, L. Gamberini, E. Hoggan, G. Jacucci, C. Katzeff, L. Broms, and L. Jonsson, “Ecofeedback on the go: Motivating energy awareness,” Computer, vol. 44, no. 5, pp. 38–45, Apr. 2011.

L. Gamberini, A. Spagnolli, N. Corradi, G. Jacucci, G. Tusa, T. Mikkola, L. Zamboni, and E. Hoggan, “Tailoring feedback to users’ actions in a persuasive game for household electricity conservation,” in Persuasive Technology. Design for Health and Safety, ser. Lecture Notes in Computer Science, M. Bang and E. Ragnemalm, Eds., vol. 7284. Berlin, Germany: Springer, 2012, pp. 100–111.

E. Costanza, S. Ramchurn, and N. Jennings, “Understanding domestic energy consumption through interactive visualization: A field study,” in Proc of the 2012 ACM Conf on Ubiquitous Computing. New York, NY, USA: ACM, 2012, pp. 216–225.

C. Chen, S. Duan, T. Cai, B. Liu, and G. Hu, “Smart energy management system for optimal microgrid economic operation,” IET Renewable Power Generation, vol. 5, no. 3, pp. 258–267, May 2011.

E. Luján, A. Otero, S. Valenzuela, E. Mocskos, L. A. Steffenel, and S. Nesmachnow, “Cloud computing for smart energy management (CC-SEM project),” in Smart Cities, S. Nesmachnow and L. Hernández Callejo, Eds., vol. 978. Cham: Springer, 2019, pp. 116–131.

J. Rabaey, M. Ammer, J. da Silva, D. Patel, and S. Roundy, “Picoradio supports ad hoc ultra-low power wireless networking,” Computer, vol. 33, no. 7, pp. 42–48, 2000.

A. Whitmore, A. Agarwal, and L. Da Xu, “The internet of things—a survey of topics and trends,” Inform Syst Front, vol. 17, no. 2, pp. 261–274, Apr. 2015.

E. Orsi and S. Nesmachnow, “Iot for smart home energy planning,” in XXIII Congreso Argentino de Ciencias de la Computación, 2017.

——, “Smart home energy planning using IoT and the cloud,” in Proc of the IEEE URUCON. IEEE, Oct. 2017, pp. 1–4.

C. A. Ramírez, R. C. Barragán, G. García-Torales, and V. M. Larios, “Low-power device for wireless sensor net65 work for smart cities,” in Proc of the IEEE MTT-S Latin America Microwave Conference (LAMC). IEEE, Dec. 2016, pp. 1–3.

Y. P. E. Wang, X. Lin, A. Adhikary, A. Grovlen, Y. Sui, Y. Blankenship, J. Bergman, and H. S. Razaghi, “A primer on 3gpp narrowband internet of things,” IEEE Commun Mag, vol. 55, no. 3, p. 117–123, Mar. 2017.

V. Nair, R. Litjens, and H. Zhang, “Assessment of the suitability of NB-IoT technology for ORM in smart grids,” in Proc of the European Conf on Networks and Communica75 tions (EuCNC). IEEE, Jun. 2018, pp. 418–423.

TSGR, “LTE; E-UTRA; Physical channels and modulation (3GPP TS 36.211 version 14.4.0 Release 14),” ETSI Standard, 2017.

——, “LTE; E-UTRA; Multiplexing and channel coding(3GPP TS 36.212 version 14.4.0 Release 14),” ETSI Standard, 2017.

E. Luján, J. A. Zuloaga Mellino, A. D. Otero, L. Rey Vega, C. G. Galarza, and E. E. Mocskos, “Extreme coverage in 5g narrowband iot: a lut-based strategy to optimize shared channels,” arXiv e-prints, p. arXiv:1908.02798, Aug. 2019.

J. A. Z. Mellino, E. Luján, A. D. Otero, E. E. Mocskos, L. R. Vega, and C. G. Galarza., “Lite NB-IoT Simulator for Uplink Layer,” in XVIII Workshop on Information Processing and Control, 2019.

E. Luján and J. A. Z. Mellino, “Lite NB-IoT NPUSCH Simulator,” NBIoT-NPUSCH-Simulator, 2018.

G. Ferrari, P. Medagliani, S. Di Piazza, and M. Martalò, “Wireless sensor networks: Performance analysis in indoor scenarios,” EURASIP Journal onWireless Communications and Networking, vol. 2007, no. 1, p. 081864, Mar 2007.

P. T. et al., “Que no se corte,” http://tecnox.exp.dc.uba. ar/exactics/index.php/Main_Page, 2018.

W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, “Short-term residential load forecasting based on lstm recurrent neural network,” IEEE T Smart Grid, vol. 10, no. 1, pp. 841–851, Jan. 2019.

K. Amarasinghe, D. L. Marino, and M. Manic, “Deep neural networks for energy load forecasting,” in Proc of the IEEE 26th Int Symp on Industrial Electronics, Jun. 2017, pp. 1483–1488.

D. Dheeru and E. Karra Taniskidou, “UCI machine learning repository,” 2018. [Online]. Available: http: //

S. Hong, “Individual household electric power consumption,” 2015. [Online]. Available: https://sunhaehong.

D. L. Marino, K. Amarasinghe, and M. Manic, “Building energy load forecasting using deep neural networks,” in Proc of IECON 2016 - 42nd Annual Conf of the IEEE Industrial Electronics Society. IEEE, Oct. 2016, pp. 7046–7051.

K. Zhou and S. Yang, “Understanding household energy consumption behavior: The contribution of energy big data analytics,” Renew Sust Energ Rev, vol. 56, pp. 810–819, Apr. 2016.

Z. J. Kolter and M. J. Johnson, “Redd: A public data set for energy disaggregation research,” in Proc of the 1st KDD Workshop on Data Mining Applications in Sustainability, 2011.

W. Skamarock, J. Klemp, J. Dudhia, D. Gill, D. Barker, M. Duda, X. Huang, W. Wang, and J. Powers, “A Description of the Advanced Research WRF Version 3,” National Center for Atmospheric Research, Tech. Note NCAR/TN- 475+STR, 2008.

N. C. for Atmospheric Research (NCAR), “Weather Research and Forecasting (WRF) Model,” forecasting-model, 2018. [Online]. Available: forecasting-model

W. F. Holmgren, R. W. Andrews, A. T. Lorenzo, and J. S. Stein, “PVLIB Python 2015,” in Proc of the 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC). IEEE, Jun. 2015, pp. 1–5.

W. F. Holmgren and D. G. Groenendyk, “An open source solar power forecasting tool using PVLIB-Python,” in Proc of the 2016 IEEE 43rd Photovoltaic Specialists Conference. IEEE, Jun. 2016, pp. 0972–0975.

SAVER-Net Project,, 2018.

N. C. for Environmental Prediction (NCEP), “NCEP Unified Post Processing System (UPP),” https://dtcenter. org/upp/users/index.php, 2017. [Online]. Available: https: //

D. P. Larson, L. Nonnenmacher, and C. F. Coimbra, “Dayahead forecasting of solar power output from photovoltaic plants in the american southwest,” Renew Energ, vol. 91, pp. 11 – 20, 2016.

E. L. Maxwell, “A quasi-physical model for converting hourly global horizontal to direct normal insolation,” Solar Energy Research Inst., Golden, CO (USA), Tech. Rep. SERI/TR-215-3087, Aug 1987.

N. C. for Environmental Prediction, “NCEP GDAS/FNL 0.25 Degree Global Tropospheric Analyses and Forecast Grids,”, Boulder CO, 2015.