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Analysis of Energy Production and Consumption Prediction Approaches in Smart Grids

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Innovations in Smart Cities and Applications (SCAMS 2017)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 37))

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Abstract

The importance of energy prediction is to ensure Load balance, storage management, relevant integration of renewable resources… There are many scientific research efforts in this field based on different statistical methods and machine learning algorithms. In this paper we analyze four of prediction process in energy prediction in Smart Grids (SGs), especially energy consumption, production or load. This analysis is based on specific criteria and underlies advantages and limitations of each one.

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References

  1. Kramer, O., Satzger, B., Lässig, J.: Power prediction in smart grids with evolutionary local kernel regression. Springer, Heidelberg (2010)

    Google Scholar 

  2. Cecati, C., Citro, C., Siano, P.: Combined operations of renewable energy systems and responsive demand in a smart grid. IEEE Trans. Sustain. Energy 2(4), 468–476 (2011)

    Article  Google Scholar 

  3. Fan, S., Hyndman, R.J.: Short-term load forecasting based on a semi-parametric additive model. IEEE Trans. Power Syst. (2010)

    Google Scholar 

  4. Sarwat, A., Amini, M., Domijan Jr., A., Damnjanovic, A., Kaleem, F.: Weather-based interruption prediction in the smart grid utilizing chronological data. State Grid Electric Power Research Institute. https://doi.org/10.1007/s40565-015-0120-4

  5. Narayanaswamy, B., Garg, V.K., Jayram, T.S.: Prediction based storage management in the smart grid. In: IEEE SmartGridComm 2012 Symposium - Support for Storage, Renewable Sources and MicroGrid. IBM Research, India Research Lab (2012)

    Google Scholar 

  6. Albadi, M.H., El-Saadany, E.F.: Overview of wind power intermittency impacts on power systems. Electr. Power Syst. Res. 80, 627–632 (2009)

    Article  Google Scholar 

  7. Aman, S., Simmhan, Y., Prasanna, V.K.: Empirical comparison of prediction methods for electricity consumption forecasting. Trans. Knowl. Data Eng. (2014)

    Google Scholar 

  8. Verbraken, T., Verbeke, W., Baesens, B.: A novel profit maximizing metric for measuring classification performance of customer churn prediction models. IEEE Trans. Knowl. Data Eng. 25(5), 961–973 (2013)

    Article  Google Scholar 

  9. Chelmis, C., Saeed, M.R., Frincu, M., Prasanna, V.K.: Curtailment Estimation Methods for Demand Response. Ming Hsieh Department of Electrical Engineering Department. University of Southern California

    Google Scholar 

  10. Aman, S., Frincuy, M., Chelmisy, C., Noory, M., Simmhanz, Y., Prasanna, V.K.: Prediction models for dynamic demand response: requirements, challenges, and insights, November 2015

    Google Scholar 

  11. Colak, I., Sagiroglu, S., Yesilbudak, M.: Data mining and wind power prediction: a literature review. Renew. Energy 46, 241–247 (2012)

    Article  Google Scholar 

  12. Rossi, F.: Apprentissage Supervise. TELECOM ParisTech, Paris (2009)

    Google Scholar 

  13. Ben-David, S., Kushilevitz, E., Mansour, Y.: Online learning versus offline learning. Mach. Learn. 29, 45–63 (1997). Kluwer Academic Publishers. Manufactured in The Netherlands

    Article  MATH  Google Scholar 

  14. Yu, W., An, D., Griffith, D., Yang, Q., Xu, G.: Towards statistical modeling and machine learning based energy usage forecasting in smart grid. In: RACS 2014 Proceedings of the 2014 ACM Research in Adaptive and Convergent Systems, vol. 15(1), March 2015

    Google Scholar 

  15. Durgabai, R.P.L.: Feature selection using ReliefF algorithm. Int. J. Adv. Res. Comput. Commun. Eng. 3(10), 8215–8218 (2014)

    Article  Google Scholar 

  16. Campos, B.P., da Silva, M.R.: Demand forecasting in residential distribution feeders in the context of smart grids

    Google Scholar 

  17. Aung, Z., Toukhy, M., Williams, J.R., Sanchez, A., Herrero, S.: Towards accurate electricity load forecasting in smart grids. In: The Fourth International Conference on Advances in Databases, Knowledge, and Data Applications, DBKDA (2012)

    Google Scholar 

  18. Mocanu, E., Nguyen, P.H., Kling, W.L., Gibescu, M.: Unsupervised energy prediction in a smart grid context using reinforcement cross-building transfer learning. Energy Build. 116, 646–655 (2016)

    Article  Google Scholar 

  19. Watkins, C.J.C.H., Dayan, P.: Q-learning. Mach. Learn. 8, 279–292 (1992). Kluwer Academic Publishers, Boston. Manufactured in The Netherlands

    MATH  Google Scholar 

  20. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, pp. 141–144, 169–172

    Google Scholar 

  21. Torrey, L., Shavlik, J.: Transfer Learning. University of Wisconsin, Madison (2009)

    MATH  Google Scholar 

  22. Hebbo, H., Kim, J.W.: Classification with deep belief networks

    Google Scholar 

  23. Koenig, S., Simmons, R.G.: Complexity analysis of real-time reinforcement learning

    Google Scholar 

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Author information

Authors and Affiliations

  1. Mohammadia School of Engineers, Mohammed 5 University, Rabat, Morocco

    Atimad El Khaouat & Laila Benhlima

Authors
  1. Atimad El Khaouat
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  2. Laila Benhlima
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Correspondence to Atimad El Khaouat .

Editor information

Editors and Affiliations

  1. Computer Sciences Department, Faculty of Sciences and Techniques, Abdelmalek Essaadi University, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Computer Sciences Department, Faculty of Sciences and Techniques, Abdelmalek Essaadi University, Tangier, Morocco

    Anouar Abdelhakim Boudhir

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El Khaouat, A., Benhlima, L. (2018). Analysis of Energy Production and Consumption Prediction Approaches in Smart Grids. In: Ben Ahmed, M., Boudhir, A. (eds) Innovations in Smart Cities and Applications. SCAMS 2017. Lecture Notes in Networks and Systems, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-74500-8_58

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  • DOI: https://doi.org/10.1007/978-3-319-74500-8_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74499-5

  • Online ISBN: 978-3-319-74500-8

  • eBook Packages: EngineeringEngineering (R0)

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