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A fog based load forecasting strategy for smart grids using big electrical data

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Abstract

Internet of things (IoT) enables the smart electrical grids (SEGs) to support a lot of tasks throughout the generation, transmission, distribution and consumption of energy. Large amounts of data are generated through these smart devices, which can be sent to the cloud for further processing. Hence, appropriate actions can be taken based on the cloud processing. However, sending the complete captured data directly to the cloud would lead to resource wastage. To overcome the cloud challenges, fog computing tier acts as a bridge in the middle between the IoT devices embedded in SEG and the cloud. Thus, a 3-tiers architecture is proposed to replace 2-tiers one. The added fog tier offers a place for collecting, computing, storing smart meter data before transmitting them to the cloud. In this paper, a new electrical load forecasting (ELF) strategy has been proposed based on the pre-mentioned three tiers architecture. The proposed strategy consists of two phases; which are; (i) data pre-processing phase (DP2) and (ii) load prediction phase (LP2). Both phases take place at a cloud data center (CDC) on the collected data, which is received from all fogs connected to the entire cloud. The aim of the data preprocessing phase is to; (i) reject outliers from the collected data, and (ii) feature selection. The main contribution of this paper lied on the feature selection, which elects only the effective features for the next load prediction phase. Features selection is an essential pre-processing operation to reduce the dataset dimensions from all irrelevant features leading to enable the ELF from taking fast and correct decision for other electrical subsystems. A new feature selection methodology called fuzzy based feature selection (FBFS) is presented, which involves two stages; feature ranking stage (FRS) and feature selection stage (FS2). FBFS is a hybrid method based on both features selection methods filter and wrapper providing fast and more accurate significant features. Then, during LP2, the filtered data is used to give a fast and accurate load prediction. Experimental results have shown that the proposed FBFS promotes the load forecasting efficiency in terms of precision, recall, accuracy and F-measure compared with recent features selection methodologies. FBFS provides the highest precision, recall, and accuracy results. On the other hand, it provides the lowest error value.

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References

  1. Ozger, M., Cetinkaya, O., Akan, O.B.: Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob. Netw. Appl. 23(4), 956–966 (2017)

    Article  Google Scholar 

  2. Saleem, Y., Crespi, N., Rehmani, M.H., Copeland, R.: Internet of things-aided smart grid: technologies, architectures, applications, prototypes, and future research directions (2017). arXiv:1704.08977

  3. Rahmani, A.M., Liljeberg, P., Preden, J., Jantsch, A.: Fog Computing in the Internet of Things, ISBN 978-3-319-57638-1, ISBN 978-3-319-57639-8 (eBook). Springer, New York (2018)

    Google Scholar 

  4. Dastjerdi, A.V., Buyya, R.: Fog computing: helping the internet of things realize its potential. Computer 49(8), 112–116 (2016)

    Article  Google Scholar 

  5. Shi, W., Dustdar, S.: The promise of edge computing. Computer 49(5), 78–81 (2016)

    Article  Google Scholar 

  6. Mahmud, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions (2016). arXiv:1611.05539

  7. Gangurde, H.D.: Feature selection using clustering approach for big data, Int. J. Comput. Appl. (0975–8887) Innovations and Trends in Computer and Communication Engineering (ITCCE), pp. 1–3 (2014)

  8. Revathi, L., Appandiraj, A.: Hadoop based parallel framework for feature subset selection in big data. Int. J. Innov. Res. Sci. 4(5), 2319–8753 (2015)

    Google Scholar 

  9. Sajadfara, N., Mab, Y.: A hybrid cost estimation framework based on feature-oriented data mining approach. Adv. Eng. Inform. 29(3), 633–647 (2015)

    Article  Google Scholar 

  10. Kumar, R., Verma, R.: Classification algorithms for data mining: a survey. Int. J. Innov. Eng. Technol. (IJIET) 1(2), 7–14 (2012)

    Google Scholar 

  11. Aziz, A.S.A., Azar, A.T., Salama, M.A.: Genetic algorithm with different feature selection techniques for anomaly detectors generation. In: Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 769–774 (2013)

  12. Abualigah, L.M., Khader, A.T., Al-Beta, M.A., Alomari, O.A.: Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst. Appl. 84, 24–36 (2017)

    Article  Google Scholar 

  13. Zhang, L., Shan, L., Wang, J.: Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion. Neural Comput. Appl. 28(9), 2795–2808 (2017)

    Article  Google Scholar 

  14. Chutia, D., Bhattacharyya, D.K., Sarma, J., Raju, P.N.L.: An effective ensemble classification framework using random forests and a correlation based feature selection technique. Trans. GIS 21, 1165–1178 (2017)

    Article  Google Scholar 

  15. Ortega, L., Han, Z.H.: Complexity theory and language development: In: Celebration of diane Larsen-freeman, John Benjamins B.V., Amsterdam, ICCN 2017028813 (2017)

  16. Mardani, A., Nilashi, M., Antucheviciene, J., Tavana, M., Bausys, R., Ibrahim, O.: Recent fuzzy generalisations of rough sets theory: a systematic review and methodological critique of the literature. Complexity 1–33, 2017 (2017)

    MathSciNet  MATH  Google Scholar 

  17. Rahmani, M., Atia, G.K.: Randomized robust subspace recovery and outlier detection for high dimensional data matrices. IEEE Trans. Signal Process. 65(6), 1580–1594 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  18. Wang, Y., Ke, W., Tao X.: A feature selection method for large-scale network traffic classification based on spark. www.mdpi.com/journal/information, 7(1) (2016)

  19. Bouaguel, W.: A new approach for wrapper feature selection using genetic algorithm for big data. Part of the Proceedings in Adaptation, Learning and Optimization book series, Intelligent and Evolutionary Systems, Springer 5, 75–83 (2016)

    Article  Google Scholar 

  20. Jiang, S.Y., Wang, L.X.: Efficient feature selection based on correlation measure between continuous and discrete features. Inf. Process. Lett. 116(2), 203–215 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  21. Shamsinejadbabki, P., Saraee, M.: A new unsupervised feature selection method for text clustering based on genetic algorithms. J. Intell. Inf. Syst. 38(3), 669–684 (2012)

    Article  Google Scholar 

  22. Wang, H., Liu, S.: An effective feature selection approach using the hybrid filter wrapper. Int. J. Hybrid Inf. Technol. 9(1), 119–128 (2016)

    Article  MathSciNet  Google Scholar 

  23. Xie, J., Lei, J., Xie, W., Shi, Y., Liu, X.: Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases. Health Inf. Sci. Syst. 1, 1–10 (2013)

    Article  Google Scholar 

  24. Wang, W., Gombault, S.: Efficient detection of DDoS attacks with important attributes. In: Proceedings of the Risks and Security of Internet and Systems on CRiSIS’08 Third International Conference, IEEE, pp. 61–67 (2008)

  25. Song, Q., Ni, J., Wang, G.: Fast clustering-based feature subset selection algorithm for high dimensional data. IEEE Trans. Knowl. Data Eng. 25(1), 1–14 (2013)

    Article  Google Scholar 

  26. Wang, K., Xu, C., Guo S.: Big data analytics for price forecasting in smart grids. In: Proceedings of the Global Communications Conference (GLOBECOM), IEEE (2016)

  27. Li, L., Ota, K., Dong, M.: When weather matters: IoT-based electrical load forecasting for smart grid. IEEE Commun. Mag. 55(10), 46–51 (2017)

    Article  Google Scholar 

  28. Okay, F.Y., Ozdemir, S.: A fog computing based smart grid model. International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet, pp. 1–6 (2016)

  29. Shahryari, K., Oghaddam, A.A.: Demand side management using the internet of energy based on fog and cloud computing. In: Proceedings of the 10th IEEE International Conference on Internet of Things (iThings 2017), pp. 1–6 (2017)

  30. Jaradat, M., Jarrah, M., Bousselham, A., Jararweh, Y., Al-Ayyouba, M.: The internet of energy: smart sensor networks and big data management for smart grid. Procedia Comput. Sci. 6, 592–597 (2015)

    Article  Google Scholar 

  31. Yu, C.N., Mirowski, P., Ho, T.K.: A sparse coding approach to household electricity demand forecasting in smart grids. IEEE Trans. Smart Grid 8(2), 738–748 (2017)

    Google Scholar 

  32. Ahmeda, A., Korresb, N.E., Ploennigsc, J., Elhadid, H., Menzela, K.: Mining building performance data for energy-efficient operation. Adv. Eng. Inform. 25(2), 341–354 (2011)

    Article  Google Scholar 

  33. Edris, A.A., D’Andrade, B.W.: Transmission grid smart technologies. The power grid, pp. 37–55. Elsevier, New York (2017)

    Chapter  Google Scholar 

  34. Dincer, I., Acar, C.: Smart energy systems for a sustainable future. Appl. Energy 194, 225–235 (2017)

    Article  Google Scholar 

  35. Jestes, J.: efficient summarization techniques for massive data. A thesis submitted to the faculty of the University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy, School of Computing, The University of Utah, (2013)

  36. Vidhya, K.A., Geetha, T.V.: Rough set theory for document clustering: a review. J. Intell. Fuzzy Syst. 32(3), 2165–2185 (2017)

    Article  Google Scholar 

  37. Gu, S., Cheng, R., Jin, Y.: Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput. 22(3), 811–822 (2018)

    Article  Google Scholar 

  38. Venkataraman, S., Selvaraj, R.: Optimal and novel hybrid feature selection framework for effective data classification. Advances in systems, control and automation, part of the lecture notes in electrical engineering book series, vol. 442, pp. 499–514. Springer, Singapore (2017)

    Chapter  Google Scholar 

  39. Pascoal, C., Oliveira, M.R., Pacheco, A., Valadas, R.: Theoretical evaluation of feature selection methods based on mutual information. Neurocomputing 226, 168–181 (2017)

    Article  Google Scholar 

  40. Li, Y.H.: Text feature selection algorithm based on Chi square rank correlation factorization. J. Interdiscip. Math. 20(1), 153–160 (2017)

    Article  Google Scholar 

  41. Rajab, K.D.: New hybrid features selection method: a case study on websites phishing. Secur Commun Netw 1–10, 2017 (2017)

    Google Scholar 

  42. Vinutha, H.P., Poornima, B.: An ensemble classifier approach on different feature selection methods for intrusion detection. Information systems design and intelligent applications, pp. 443–451. Springer, New York (2018)

    Google Scholar 

  43. Djellali, H., Zine, N.G., Azizi, N.: Two stages feature selection based on filter ranking methods and SVMRFE on medical applications. Modelling and implementation of complex systems, pp. 281–293. Springer, New York (2016)

    Chapter  Google Scholar 

  44. Saleh, A.I., El Desouky, A.I., Ali, S.H.: Promoting the performance of vertical recommendation systems by applying new classification techniques. Knowl Based Syst. 75, 192–223 (2015)

    Article  Google Scholar 

  45. Rebrovs, O.U., Kuļešova, G.: Comparative analysis of fuzzy set defuzzification methods in the context of ecological risk assessment. Inf. Technol. Manag. Sci. 20(1), 25–29 (2017)

    Google Scholar 

  46. Giarratano, J., Riley, G.: Expert systems: principles and programming, 4th edn. Course Technology Inc, Boston (2004)

    Google Scholar 

  47. Feng, X., Li, S., Yuan, C., Zeng, P., Sun, Y.: Prediction of slope stability using naive Bayes classifier. KSCE J. Civ. Eng. 22(3), 941–950 (2018)

    Article  Google Scholar 

  48. European Network on Intelligent TEchnologies for Smart Adaptive Systems. http://www.eunite.org/. The competition page is: http://neuron.tuke.sk/competition/

  49. Zdravevski, E., Lameski, P., Kulakov, A., Jakimovski, B., Filiposka, S., Trajanov, D.: Feature ranking based on information gain for large classification problems with MapReduce. Trustcom/BigDataSE/ISPA, IEEE (2015)

  50. Jin, C., Ma, T., Hou, R.: Chi square statistics feature selection based on term frequency and distribution for text categorization. IETE J. Res. 61(4), 351–362 (2015)

    Article  Google Scholar 

  51. Alyam, R., Alhajja, J., Alnajran, B., Elaalam, I., Alqahtan, A., Aldhaffer, N., Owolab, T.O., Olatun, S.O.: Investigating the effect of correlation based feature selection on breast cancer diagnosis using artificial neural network and support vector machines. In: Proceedings of the International Conference on Informatics, Health & Technology (ICIHT), IEEE (2017)

  52. Wang, K., Xu, C., Zhang, Y., Guo, S., Zomaya, A.Y.: Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans Big Data (2017). https://doi.org/10.1109/TBDATA.2017.2723563

    Article  Google Scholar 

  53. Nazar, N.B., Senthilkumar, R.: An online approach for feature selection for classification in big data. Turk. J. Electr. Eng. Comput. Sci. 2017(25), 163–171 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Engineering and Systems Department, Faculty of Engineering, Mansoura University, Po. Box: 35516, Mansoura, Egypt

    Asmaa H. Rabie, Hesham A. Ali & Ahmed I. Saleh

  2. Communications & Electronics Engineering Department, Delta Higher Institute for Engineering &Technology, Mansoura, Egypt

    Shereen H. Ali

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  1. Asmaa H. Rabie
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  2. Shereen H. Ali
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  3. Hesham A. Ali
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  4. Ahmed I. Saleh
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Correspondence to Ahmed I. Saleh.

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Rabie, A.H., Ali, S.H., Ali, H.A. et al. A fog based load forecasting strategy for smart grids using big electrical data. Cluster Comput 22, 241–270 (2019). https://doi.org/10.1007/s10586-018-2848-x

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