Skip to main content
SpringerLink
Log in

Short-Term Residential Load Forecasting Using 2-Step SARIMAX

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

In contrast to city-level and larger aggregate-level load forecasting, load forecasting for residential customers is a much more challenging problem because residential loads are much more volatile. In order to forecast the residential load at one-hour interval 24-h loads the day before, a 2-Step SARIMAX method for residential load forecasting is proposed in this study. The forecasting performance of the proposed method is compared with the existing forecasting methods including SARIMA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Korea Electric Power Corporation (2021) 2020 year KEPCO in Brief

  2. Ministry of Trade, Industry and Energy (2020) 9th Basic plan for long-term electricity demand and supply (2020~2034)

  3. Deb C, Zhang F, Yang J, Lee SE, Shah KW (2017) A review on time series forecasting techniques for building energy consumption. Renew Sustain Energy Rev 74:902–924

    Article  Google Scholar 

  4. Ahmad MI (2017) Seasonal decomposition of electricity consumption data. Rev Integr Bus Econ Res 6(4):902–924

    Google Scholar 

  5. Hussain T, Muhammad K, Ser JD, Baik SW, de Albuquerque VHC (2020) Intelligent embedded vision for summarization of multiview videos in IIOT. IEEE Trans Ind Inf 16(4):2592–2602

    Article  Google Scholar 

  6. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the advances neural information processing systems, pp 1097–1105

  7. Jeong HC, Jung J, Kang BO (2018) Development of ARIMA-based forecasting algorithms using meteorological indices for seasonal peak load. Trans Korean Inst Electr Eng 67:1257–1264

    Google Scholar 

  8. Juberias G, Yunta R, Moreno JG, Mendivil C (1999) A new ARIMA model for hourly load forecasting. In: Transmission and distribution conference. IEEE, pp 314–319

  9. Elamin N, Fukushige M (2018) Modeling and forecasting hourly electricity demand by SARIMAX with interactions. Energy 165(Part B):257–268

    Article  Google Scholar 

  10. Shi H, Xu M, Li R (2018) Deep learning for household load forecasting—a novel pooling deep RNN. IEEE Trans Smart Grid 9(5):5271–5280

    Article  Google Scholar 

  11. Quilumba FL, Lee WJ, Huang H, Wang DY, Szabados RL (2015) Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities. IEEE Trans Smart Grid 6(2):911–918

    Article  Google Scholar 

  12. Hong T, Wang P, Pahwa A, Gui M, Hsiang SM (2010) Cost of temperature history data uncertainties in short term electric load forecasting. In: Proceedings of IEEE 11th international conference on probabilistic methods applied to power systems (PMPS), Singapore, pp 212–217

  13. Hong T, Wang P, White L (2015) Weather station selection for electric load forecasting. Int J Forecast 31(2):286–295

    Article  Google Scholar 

  14. Chen Y et al (2010) Short-term load forecasting: similar day-based wavelet neural networks. IEEE Trans Power Syst 25(1):322–330

    Article  Google Scholar 

  15. Al-Otaibi R, Jin N, Wilcox T, Flach P (2016) Feature construction and calibration for clustering daily load curves from smart-meter data. IEEE Trans Ind Inf 12(2):645–654

    Article  Google Scholar 

  16. Shi D, Li R, Shi R, Li F (2014) Analysis of the relationship between load profile and weather condition. In: Proceedings of IEEE PES Gen. meeting Conf. Expo., National Harbor, MD, USA, pp 1–5

  17. Aggarwal SK, Kumar M (2016) ANN based residential demand forecasting using weather and special day information. In: 7th India international conference on power electronics (IICPE), pp 1–5

  18. Cho MY, Hwang JC, Chen CS (1995) Customer short term load forecasting by using ARIMA transfer function model. In: IEEE conference on energy management and power delivery, vol 1, pp 317–322

  19. Lee G-C, Han J (2020) Forecasting gas demand for power generation with SARIMAX models. Korean Manag Sci Rev 67–78

  20. Box GEP, Jenkins GM, Reinsel GC (2008) Time series analysis forecasting and control, 4th edn. John Wiley and Sons

  21. Jeong HC, Jang M, Kim T, Joo S-K (2021) Clustering of load profiles of residential customers using extreme points and demographic characteristics. Electronics 10

  22. Zeng J, Wang J, Guo L, Fan G, Zhang K, Gui G (2019) Cell scene division and visualization based on autoencoder and K-means algorithm. IEEE Access 7:165217–165225

    Article  Google Scholar 

  23. Enders W (2004) Applied econometric time series, 2nd edn. Wiley

  24. Al-Musaylh MS, Deo RC, Adamowski JF, Li Y (2018) Short-term electricity demand forecasting with MARS, SVR, and ARIMA models using aggregated demand data in Queensland, Australia. Elsevier 35:1–16

Download references

Acknowledgements

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry & Energy(MOTIE) of the Republic of Korea (No. 20181210301430). This research was supported by the Basic Research Program through the National Research Foundation of Korea(NRF) funded by the MSIT(No. 2020R1F1A1075872).

Author information

Authors and Affiliations

  1. The School of Electrical Engineering, Korea University, Seoul, 02841, Korea

    Taegon Kim, Minseok Jang, Hyun Cheol Jeong & Sung-Kwan Joo

Authors
  1. Taegon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minseok Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyun Cheol Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sung-Kwan Joo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sung-Kwan Joo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, T., Jang, M., Jeong, H.C. et al. Short-Term Residential Load Forecasting Using 2-Step SARIMAX. J. Electr. Eng. Technol. 17, 751–758 (2022). https://doi.org/10.1007/s42835-021-00917-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-021-00917-z

Keywords

Search

Navigation