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Prediction Model of City Electricity Consumption

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Abstract

Power load forecasting is a series of forecasting work for power load. From the point of view of forecasting objects, power load forecasting includes the prediction of future power demand and the prediction of load curve, which provides a reliable decision-making basis for power system planning and operation. This chapter focuses on the analysis and prediction of short-term electricity consumption and long-term electricity consumption data in urban areas. The short-term prediction of electricity consumption is carried out by using the time-series model, and the periodic characteristics of the long-term power consumption series are further mined by the seasonal time-series model. On this basis, the uncertainty interval prediction of electricity consumption data is realized by using heteroscedasticity model, and the distributed computing strategies of these methods under the framework of big data are provided.

References

  1. Bianchi FM, Maiorino E, Kampffmeyer MC, et al (2017) An overview and comparative analysis of recurrent neural networks for short term load forecasting [J]. arXiv preprint arXiv: 170504378Google Scholar
  2. Brockwell PJ, Davis RA (2009) Introduction to time series and forecasting. Springer, BerlinzbMATHGoogle Scholar
  3. Chhay L, Reyad MAH, Suy R, Islam MR, Mian MM (2018) Municipal solid waste generation in China: Influencing factor analysis and multi-model forecasting. J Mater Cycles Waste Manag 20(3):1–10CrossRefGoogle Scholar
  4. Hilbert S (2017) Correlation coefficient. Springer, BerlinCrossRefGoogle Scholar
  5. Jaffel I, Taouali O, Harkat MF, Messaoud H (2017) Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring. Int J Adv Manuf Technol 88(9–12):3265–3279CrossRefGoogle Scholar
  6. Kavaklioglu K (2011) Modeling and prediction of Turkey’s electricity consumption using support vector regression. Appl Energy 88(1):368–375CrossRefGoogle Scholar
  7. Mu Y, Liu X, Wang L (2018) A Pearson’s correlation coefficient based decision tree and its parallel implementation. Inf Sci 435:40–58MathSciNetCrossRefGoogle Scholar
  8. Oprea SV, Pîrjan A, Căruțașu G, Petroșanu DM, Bâra A, Stănică JL et al (2018) Developing a mixed neural network approach to forecast the residential electricity consumption based on sensor recorded data. Sensors 18(5):1443CrossRefGoogle Scholar
  9. Persio LD, Honchar O (2017) Analysis of recurrent neural networks for short-term energy load forecasting. In: American Institute of Physics Conference Series. AIP, College ParkGoogle Scholar
  10. Rahman S, Shrestha G (1991) A priority vector based technique for load forecasting. IEEE Trans Power Syst 6(4):1459–1465CrossRefGoogle Scholar
  11. Sadaei HJ, Guimarães FG, Silva CJD, Lee MH, Eslami T (2017) Short-term load forecasting method based on fuzzy time series, seasonality and long memory process. Int J Approx Reason 83(C):196–217MathSciNetCrossRefGoogle Scholar
  12. Saeed Madani S (1991) Electric load forecasting using an artificial neural network. IEEE Trans Power Syst 6(2):442–449CrossRefGoogle Scholar
  13. Shi H, Xu M, Ran L (2017) Deep learning for household load forecasting—a novel pooling deep RNN. IEEE Trans Smart Grid 99:1–1Google Scholar
  14. Tamura Y, Sato T, Ooe M, Ishiguro M (2010) A procedure for tidal analysis with a Bayesian information criterion. Geophys J Int 104(3):507–516CrossRefGoogle Scholar
  15. Wu L, Gao X, Xiao Y, Yang Y, Chen X (2018) Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China. Energy 157:327–335CrossRefGoogle Scholar
  16. Zhou X, Guo T, Jiao J (2012) Application of trend extrapolation method to spectrum analysis of microtremor signal. Springer, Berlin, pp 653–658Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. and Science Press 2020

Authors and Affiliations

  • Hui Liu
    • 1
  1. 1.School of Traffic and Transportation EngineeringCentral South UniversityChangshaChina

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