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Analysis of the Efficiency-Energy with Regression and Classification in Household Using K-NN

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Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11633))

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Abstract

This paper aims to study energy consumption in a house. Home energy management system (HEMS) has become very important, because energy consumption of a residential sector accounts for a significant amount of total energy consumption. However, a conventional HEMS has some architectural limitations among dimensional variables reusability and interoperability. Furthermore, the cost of implementation in HEMS is very expensive, which leads to the disturbance of the spread of a HEMS. Therefore, this study proposes an Internet of Things (IoT) based HEMS with lightweight photovoltaic (PV) system over dynamic home area networks (DHANs), which enables the construction of a HEMS to be scalable reusable and interoperable. The study suggests a technique for decreasing cost of energy that HEMS is using and various perspectives in system. The method that proposed is K-NN (K-Nearest Neighbor) which helps us to analyze the classification and regression datasets. This paper has the result from the data relevant in October 2018 from some buildings of Nanjing University of Information Science and Technology. That dataset allowed us to make analysis of electric energy consumption of each home equipment used and to make a simulation of the energy needed for each apparatus. Finally, we succeeded to find the algorithm which is suitable for efficiency-electric energy.

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References

  1. Kamoto, K.M., Liu, Q., Liu, X.: Unsupervised energy disaggregation of home appliances. In: Sun, X., Chao, H.-C., You, X., Bertino, E. (eds.) ICCCS 2017. LNCS, vol. 10602, pp. 398–409. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68505-2_34

    Chapter  Google Scholar 

  2. Kolter, J.Z., Johnson, M.J.: REDD : a public data set for energy disaggregation research. In: SustKDD Work, CA, USA, pp. 1–4 (2011)

    Google Scholar 

  3. Singh, P., Yadav, R.: Energy efficient and delay based on PSO with LST algorithm for wireless sensor network. In: International Conference on Computational Science and Engineering, vol. 6, pp. 2094–2100 (2016)

    Google Scholar 

  4. Paper, C.: Extensive survey on k-means clustering using mapreduce in datamining. In: International Conference on Electronics and Communication Systems, pp. 2–5 (2016)

    Google Scholar 

  5. Pereira, L., Quintal, F., Goncalves, R., Nunes, N.J.: SustData: a public dataset for ICT4S electric energy research. In: Proceedings of the 2014 Conference ICT for Sustainability, pp. 359–368 (2014)

    Google Scholar 

  6. Jamsandekar, S.S., Mudholkar, R.R.: Fuzzy inference rule generation using genetic algorithm variant. IOSR J. Comput. Eng. 17, 9–16 (2015)

    Google Scholar 

  7. Medasani, S., Kim, J.: An overview of membership function generation techniques for pattern recognition. Int. J. Approx. Reason. 19, 391–417 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  8. Liu, Y., Peng, H., Wang, J.: Verifiable diversity ranking search over encrypted outsourced data. Comput. Mater. Contin. 55, 37–57 (2018)

    Article  Google Scholar 

  9. Meng, R., Rice, S.G., Wang, J., Sun, X.: A fusion steganographic algorithm based on faster R-CNN. Comput. Mater. Contin. 55(1), 1–16 (2018)

    Google Scholar 

  10. Wu, C., Zapevalova, E., Chen, Y., Li, F.: Time optimization of multiple knowledge transfers in the big data environment. Comput. Mater. Contin. 54(3), 269–285 (2018)

    Google Scholar 

  11. Ghosh, S., Biswas, S., Sarkar, D., Sarkar, P.P.: Mining frequent itemsets using genetic algorithm. Int. J. Artif. Intell. Appl. 1(4), 133–143 (2010)

    Google Scholar 

  12. Marmelstein, E.: Application of genetic algorithms to data mining. In: MAICS-97 Proceedings, pp. 53–57 (1997)

    Google Scholar 

  13. Prabhat, N.: A genetic-fuzzy algorithm to discover fuzzy classification rules for mixed attributes datasets. Int. J. Comput. Appl. 34, 15–22 (2011)

    Google Scholar 

  14. Liu, Y., Qiu, B., Fan, X., Zhu, H., Han, B.: Review of smart home energy management systems. Energy Procedia 104, 504–508 (2016)

    Article  Google Scholar 

  15. Erol-Kantarci, M., Mouftah, H.T.: Energy-efficient information and communication infrastructures in the smart grid: a survey on interactions and open issues. IEEE Commun. Surv. Tutor. 17, 179–197 (2015)

    Article  Google Scholar 

  16. Rogozhnikov, A.: Machine Learning in High Energy Physics. University of Landon, Queen Mary (2015)

    Google Scholar 

  17. Wang, Z., Ling, C.: On the geometric ergodicity of metropolis-hastings algorithms for lattice Gaussian sampling. IEEE Trans. Inf. Theory 64, 738–751 (2018)

    Article  MATH  MathSciNet  Google Scholar 

  18. Li, D., Zhang, G., Xu, Z., Lan, Y., Shi, Y.: Modelling the roles of cewebrity trust and platform trust in consumers’ propensity of live-streaming: an extended TAM method. Comput. Mater. Contin. 55, 137–150 (2018)

    Google Scholar 

  19. Zeng, D., Dai, Y., Li, F., Sherratt, R.S., Wang, J.: Adversarial learning for distant supervised relation extraction. Comput. Mater. Contin. 55, 121–136 (2018)

    Google Scholar 

  20. Kornaropoulos, E.M., Tsakalides, P.: A novel kNN classifier for acoustic vehicle classification based on alpha-stable statistical modeling. In: IEEE Workshop on Statistical Signal Processing Proceedings, pp. 1–4 (2009)

    Google Scholar 

  21. Chen, Q., Li, D., Tang, C.K.: KNN matting. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2175–2188 (2013)

    Article  Google Scholar 

  22. Bhatia, N., Vandana: Survey of nearest neighbor techniques. (IJCSIS) Int. J. Comput. Sci. Inf. Secur. 8(2), 302–305 (2010)

    Google Scholar 

  23. García, C., Gómez, I.: Algoritmos de aprendizaje: knn & kmeans. Univ. Carlos III Madrid, pp. 1–8 (2006)

    Google Scholar 

  24. Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit. 40(7), 2038–2048 (2007)

    Article  MATH  Google Scholar 

  25. Parry, R.M., et al.: k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction. Pharmacogenomics J. 10(4), 292–309 (2010)

    Article  Google Scholar 

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Acknowledgement

This work has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 701697, Major Program of the National Social Science Fund of China (Grant No. 17ZDA092), Basic Research Programs (Natural Science Foundation) of Jiangsu Province (BK20180794), 333 High-Level Talent Cultivation Project of Jiangsu Province (BRA2018332) and the PAPD fund.

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Authors and Affiliations

  1. School of Electrical Engineering, University of Jinan, Jinan, China

    Mingxu Sun

  2. School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, UK

    Xiaodong Liu

  3. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Scholas Mbonihankuye

  4. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Scholas Mbonihankuye

Authors
  1. Mingxu Sun
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  2. Xiaodong Liu
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  3. Scholas Mbonihankuye
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Corresponding author

Correspondence to Scholas Mbonihankuye .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Sun, M., Liu, X., Mbonihankuye, S. (2019). Analysis of the Efficiency-Energy with Regression and Classification in Household Using K-NN. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_31

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  • DOI: https://doi.org/10.1007/978-3-030-24265-7_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24264-0

  • Online ISBN: 978-3-030-24265-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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