Advertisement

Day-Ahead Load Forecasting Based on Conditional Linear Predictions with Smoothed Daily Profile

  • Sunme Park
  • Kanggu Park
  • Euiseok HwangEmail author
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

In a smart grid, load forecasting has a crucial role in the efficient and sustainable operation of the power system by reducing uncertainties and enabling a pre-coordination to avoid any potential problems. This study proposes day-ahead load forecasting based on a statistical approach using the records of the previous days’ pre-processed through a smoothing approach. A prediction exploiting the latest available observations has the advantage of recency effect. However, the direct use of a full-day profile may cause a wide variation in the predicted outcomes owing to noisy and correlated observations. To avoid an undesired correlation effect, binning is employed to smooth the profile of the previous day, thereby enhancing the inter-day correlations. For a performance evaluation, the 3.5-year record of the 15-min sampled electric power of a campus was investigated for day-ahead load forecasting based on a correlation analysis between days. The results indicate that the inter-day correlation in a smoothed profile is improved compared to that in a raw data and that the day-ahead load forecasting yields smaller prediction errors on average with less variability through the proposed binning approach.

Keywords

Day-ahead load forecasting Data binning Correlation analysis Conditional linear regression model Smoothing effect 

Notes

Acknowledgements

This work was supported by GIST Research Institute (GRI) grant funded by the GIST in 2018 and by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20171210200810).

References

  1. 1.
    Global Energy Statistical Yearbook. Enerdata (2018). https://yearbook.enerdata.net. Accessed on September 2018Google Scholar
  2. 2.
    Energy technology perspectives 2017: Catalysing energy technology transformations, IEA (2017)Google Scholar
  3. 3.
    Zhang, Y., Mao, M., Ding, M., Chang, L.: Study of energy management system for distributed generation systems. In: Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, DRPT 2008, pp. 2465–2469. IEEE, Piscataway (2008)Google Scholar
  4. 4.
    Siano, P.: Demand response and smart grids: a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)CrossRefGoogle Scholar
  5. 5.
    O’Connell, S., Riverso, S.: Flexibility analysis for smart grid demand response (2017). Preprint arXiv:1704.01308Google Scholar
  6. 6.
    Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., Zhao, X.: A review of data-driven approaches for prediction and classification of building energy consumption. Renew. Sustain. Energy Rev. 82, 1027–1047 (2018)CrossRefGoogle Scholar
  7. 7.
    Hong, T., Gui, M., Baran, M.E., Willis, H.L.: Modeling and forecasting hourly electric load by multiple linear regression with interactions. In: 2010 IEEE Power and Energy Society General Meeting, pp. 1–8. IEEE, Piscataway (2010)Google Scholar
  8. 8.
    Yarbrough, I., Sun, Q., Reeves, D.C., Hackman, K., Bennett, R., Henshel, D.S.: Visualizing building energy demand for building peak energy analysis. Energy Build. 91, 10–15 (2015)CrossRefGoogle Scholar
  9. 9.
    Amber, K.P., Aslam, M.W., Mahmood, A., Kousar, A., Younis, M.Y., Akbar, B., Chaudhary, G.Q., Hussain, S.K.: Energy consumption forecasting for university sector buildings. Energies 10(10), 1579 (2017)CrossRefGoogle Scholar
  10. 10.
    Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W.: A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74, 902–924 (2017)CrossRefGoogle Scholar
  11. 11.
    Taylor, J.W.: Triple seasonal methods for short-term electricity demand forecasting. Eur. J. Oper. Res. 204(1), 139–152 (2010)CrossRefGoogle Scholar
  12. 12.
    Dudek, G.: Pattern-based local linear regression models for short-term load forecasting. Electr. Power Syst. Res. 130, 139–147 (2016)CrossRefGoogle Scholar
  13. 13.
    Jalil, N.A.A., Ahmad, M.H., Mohamed, N.: Electricity load demand forecasting using exponential smoothing methods. World Appl. Sci. J. 22(11), 1540–1543 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Gwangju Institute of Science and TechnologyGwangjuSouth Korea

Personalised recommendations