Abstract
With the increasing use of electrical devices, cities consume more energy to sustain their daily activities, facing more challenges associated with energy control and distribution. This chapter revisits a previously proposed architecture to extract, load, transform, mine and forecast Big Data within a Smart City context, in order to discuss the adequacy of NoSQL databases to deliver a Smart City service that reinvents the traditional energy bill, using web and mobile applications. Citizens will benefit from a new form of self-monitoring their electricity and gas consumption, by comparing themselves to others within their cluster or region and by forecasting future energy consumptions. Moreover, the service also delivers to energy providers and cities a smarter overview of the energy landscape. The technological architecture was previously validated using simulated data from the United States of America (USA), due to its open availability, and revealed very satisfactory results concerning the performance of clustering and time series forecasting models. This chapter extends the previously proposed technological architecture, by providing real-time concurrent web and mobile access to citizens, while presenting a broad review of several NoSQL benchmarks available in the scientific community, knowledge that is essential in the adoption of a specific database to support these web and mobile applications.
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References
Vilajosana I, Llosa J, Martinez B, Domingo-Prieto M, Angles A, Vilajosana X (2013) Bootstrapping smart cities through a self-sustainable model based on big data flows. IEEE Commun Mag 51(6):128–134
Hedlund J (2013) The smart city: using IT to make cities more livable
Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19(2):171–209
Krishnan K (2013) data warehousing in the age of big data, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco
Zikopoulos P, Eaton C (2011) Understanding big data: analytics for enterprise class hadoop and streaming data, 1st edn. McGraw-Hill Osborne Media, New York
Costa C, Santos MY (2015) Improving cities sustainability through the use of data mining in a context of big city data. In: Proceedings of The World Congress on Engineering 2015, London, U.K., (Lecture Notes in Engineering and Computer Science) pp 320–325
Gama J (2010) Knowledge discovery from data streams. Taylor & Francis Group, Boca Raton
Commercial and Residential Reference Building Models (2013) Commercial and residential hourly load profiles for all TMY3 locations in the United States’, Catalog, 2013 [Online]. Available: http://en.openei.org/doe-opendata/dataset/commercial-and-residential-hourly-load-profiles-for-all-tmy3-locations-in-the-united-states. Accessed 01 Dec 2014
Xie Y, Zheng H, Zhang L-Z (2007) Electricity price forecasting by clustering-LSSVM. In: International power engineering conference, IPEC 2007, pp 697–702
Zhou H, Wu XH, Li XG (2011) An ANFIS model of electricity price forecasting based on subtractive clustering. In: 2011 IEEE power and energy society general meeting, pp 1–5
Azevedo F, Vale ZA (2006) Forecasting electricity prices with historical statistical information using neural networks and clustering techniques. In: Power systems conference and exposition, 2006. PSCE ’06. 2006 IEEE PES, pp 44–50
Alzate C, Sinn M (2013) Improved electricity load forecasting via Kernel Spectral clustering of smart meters. In: 2013 IEEE 13th international conference on data mining (ICDM), pp 943–948
Gu Y-D, Cheng J-Z, Wang Z-Y (2011) An fuzzy forecasting algorithm for short term electricity loads based on partial clustering. In: 2011 international conference on machine learning and cybernetics (ICMLC), 2011, vol 4, pp 1560–1565
Alahakoon D, Yu X (2013) Advanced analytics for harnessing the power of smart meter big data. In: 2013 IEEE international workshop on intelligent energy systems (IWIES), pp 40–45
Simmhan Y, Aman S, Kumbhare A, Liu R, Stevens S, Zhou Q, Prasanna V (2013) Cloud-based software platform for big data analytics in smart grids. Comput Sci Eng 15(4):38–47
Arenas-MartĂnez M, Herrero-Lopez S, Sanchez A, Williams JR, Roth P, Hofmann P, Zeier A (2010) A comparative study of data storage and processing architectures for the smart grid. In: 2010 first IEEE international conference on smart grid communications (SmartGridComm), pp 285–290
Mayilvaganan M, Sabitha M (2013) A cloud-based architecture for big-data analytics in smart grid: a proposal. In: 2013 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–4
Abramova V, Bernardino J, Furtado P (2014) Which NoSQL database? A performance overview. Open J. Databases 1(2):17–24
Leavitt N (2010) Will NoSQL databases live up to their promise? Computer 43(2):12–14
Tiwari S (2011) Professional NoSQL. Wiley, New York
Tudorica BG, Bucur C (2011) A comparison between several NoSQL databases with comments and notes. In: 2011 10th Roedunet international conference (RoEduNet), pp 1–5
Cooper BF (2015) Yahoo! cloud serving benchmark, 31 Mar 2010 [Online]. Available: https://s.yimg.com/ge/labs/v1/files/ycsb-v4.pdf. Accessed 11 Mar 2015
Cooper BF, Silberstein A, Tam E, Ramakrishnan R, Sears R (2010) Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM symposium on cloud computing, New York, NY, USA, pp 143–154
Kashyap S, Zamwar S, Bhavsar T, Singh S (2013) Benchmarking and analysis of NoSQL technologies. Int J Emerg Technol Adv Eng 3(9):422–426
Dory T, MejĂas B, Van Roy P, Tran N-L (2011) Comparative elasticity and scalability measurements of cloud databases. In: Proceedigs of the 2nd ACM symposium on cloud computing (SoCC), vol 11
Rabl T, GĂ³mez-Villamor S, Sadoghi M, MuntĂ©s-Mulero V, Jacobsen H-A, Mankovskii S (2012) Solving big data challenges for enterprise application performance management. Proc VLDB Endow 5(12):1724–1735
Dede E, Govindaraju M, Gunter D, Canon RS, Ramakrishnan L (2013) Performance evaluation of a mongodb and hadoop platform for scientific data analysis. In: Proceedings of the 4th ACM workshop on scientific cloud computing, pp 13–20
Chevalier M, El Malki M, Kopliku A, Teste O, Tournier R (2015) Benchmark for OLAP on NoSQL technologies comparing NoSQL multidimensional data warehousing solutions. In: 2015 IEEE 9th international conference on research challenges in information science (RCIS), pp 480–485
Abubakar Y, Adeyi TS, Auta IG (2014) Performance evaluation of NoSQL systems using YCSB in a resource austere environment. Perform Eval 7(8)
Acknowledgments
This work was supported by FCT—FundaĂ§Ă£o para a CiĂªncia e Tecnologia, within the Project Scope: UID/CEC/00319/2013 and funded by the SusCity project, MITP-TB/CS/0026/2013.
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Costa, C., Santos, M.Y. (2016). Reinventing the Energy Bill in Smart Cities with NoSQL Technologies. In: Ao, Si., Yang, GC., Gelman, L. (eds) Transactions on Engineering Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-10-1088-0_29
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DOI: https://doi.org/10.1007/978-981-10-1088-0_29
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