Skip to main content

Advertisement

Springer Nature Link
Log in

Fuzzy-based weighting long short-term memory network for demand forecasting

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

One of the main challenges in short-term electrical load forecasting is extraction of nonlinear relationships and complex dependencies among different time instances of the load time series. To deal with this difficulty, a hybrid forecasting method is proposed in this paper that uses the fuzzy expert systems and deep learning methods. In the first step, dependency of previous time instances to the next instance to be load forecasted is achieved through a fuzzy system with 125 rules. Then, the obtained weights are used beside the actual load values as the input of a long short-term memory network for load forecasting. The obtained results on two popular datasets show the superior performance of the proposed method in terms of various evaluation measures.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

No new data is used in this paper. The datasets used for the experiments are benchmark datasets.

References

  1. Li Y, Yang R, Guo P (2020) Spark-based parallel OS-ELM algorithm application for short-term load forecasting for massive user data. Electric Power Comp Syst 48(6–7):603–614

    Article  Google Scholar 

  2. Gul MJ, Urfa GM, Paul A et al (2021) Mid-term electricity load prediction using CNN and Bi-LSTM. J Supercomput 77:10942–10958

    Article  Google Scholar 

  3. Moral-Carcedo J, Pérez-García J (2017) Integrating long-term economic scenarios into peak load forecasting: an application to Spain. Energy 140(1):682–695

    Article  Google Scholar 

  4. Zhang Z, Li C, Cao Y, Tang L, Li J, Wu B (2012) Credibility assessment of short-term load forecast in power system. IEEE PES Innovative Smart Grid Technologies, Tianjin, pp 1–5

    Google Scholar 

  5. Imani M, Ghassemian H (2018) Electrical load forecasting using customers clustering and smart meters in internet of things. In: 9th International symposium on telecommunications (IST2018), Tehran, Iran, pp 113–117

  6. Al-Hamadi HM, Soliman SA (2004) Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model. Electric Power Syst Res 68(1):47–59

    Article  Google Scholar 

  7. Wang Q, Song X, Li R (2018) A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production. Energy 165(2):1320–1331

    Article  Google Scholar 

  8. Keshvari R, Imani M, Parsa Moghaddam M (2022) A clustering-based short-term load forecasting using independent component analysis and multi-scale decomposition transform. J Supercomput 78:7908–7935

    Article  Google Scholar 

  9. Niu D, Wang Y, Wu DD (2010) Power load forecasting using support vector machine and ant colony optimization. Expert Syst Appl 37(3):2531–2539

    Article  Google Scholar 

  10. Anandhi V, ManickaChezian R (2013) Support vector regression to forecast the demand and supply of pulpwood. Int J Future Comput Commun 2(3):266–269

    Article  Google Scholar 

  11. Fentis A, Bahatti L, Mestari M, Chouri B (2017) Short-term solar power forecasting using support vector regression and feed-forward NN. In: 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS), Strasbourg, pp 405–408

  12. Jiang H, Zhang Y, Muljadi E, Zhang JJ, Gao DW (2018) A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization. IEEE Trans Smart Grid 9(4):3341–3350

    Article  Google Scholar 

  13. Hu Q, Zhang S, Yu M, Xie Z (2016) Short-term wind speed or power forecasting with heteroscedastic support vector regression. IEEE Trans Sustain Energy 7(1):241–249

    Article  Google Scholar 

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

    Google Scholar 

  15. Ertuğrul ÖF, Tekin R, Kaya Y (2017) Randomized feed-forward artificial neural networks in estimating short-term power load of a small house: a case study. In: 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, pp 1–5

  16. Cecati C, Kolbusz J, Różycki P, Siano P, Wilamowski BM (2015) A Novel RBF training algorithm for short-term electric load forecasting and comparative studies. IEEE Trans Ind Electron 62(10):6519–6529

    Article  Google Scholar 

  17. Wang Y, Luo X, Ding L, Fu S, Wei X (2019) Detection based visual tracking with convolutional neural network. Knowl Based Syst 175:62–71

    Article  Google Scholar 

  18. Imani M, Ghassemian H (2019) Sequence to image transform based convolutional neural network for load forecasting. In: 27th Iranian Conference on Electrical Engineering (ICEE 2019), Yazd, Iran

  19. Chen K, Chen K, Wang Q, He Z, Hu J, He J (2019) Short-term load forecasting with deep residual networks. IEEE Trans Smart Grid 10(4):3943–3952

    Article  Google Scholar 

  20. Fekri MN, Patel H, Grolinger K, Sharma V (2021) Deep learning for load forecasting with smart meter data: online adaptive recurrent neural network. Appl Energy 282(3):116117

    Google Scholar 

  21. 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 

  22. Xu X, Gong M, Huang L, Liu A (2019) High-precision power load forecasting using real-time temperature information and deep learning method. Electric Power Comp Syst 47(16–17):1574–1583

    Article  Google Scholar 

  23. Černe G, Dovžan D, Škrjanc I (2018) Short-term load forecasting by separating daily profiles and using a single fuzzy model across the entire domain. IEEE Trans Ind Electron 65(9):7406–7415

    Article  Google Scholar 

  24. Dudek G (2017) Artificial immune system with local feature selection for short-term load forecasting. IEEE Trans Evol Comput 21(1):116–130

    Article  Google Scholar 

  25. Li S, Wang P, Goel L (2016) A novel wavelet-based ensemble method for short-term load forecasting with hybrid neural networks and feature selection. IEEE Trans Power Syst 31(3):1788–1798

    Article  Google Scholar 

  26. Liu Q, Shen Y, Wu L, Li J, Zhuang L, Wang S (2018) A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting. CSEE J Power Energy Syst 4(2):226–237

    Article  Google Scholar 

  27. Imani M, Ghassemian H (2019) Lagged load wavelet decomposition and LSTM networks for short-term load forecasting. In: International Conference on Pattern Recognition and Image Analysis (IPRIA), Tehran, Iran

  28. Zang H, Xu R, Cheng L, Ding T, Liu L, Wei Z, Sun G (2021) Residential load forecasting based on LSTM fusing self-attention mechanism with pooling. Energy 229:120682

    Article  Google Scholar 

  29. Mughees N, Mohsin SA, Mughees A, Mughees A (2021) Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting. Expert Syst Appl 175:114844

    Article  MATH  Google Scholar 

  30. Qin J, Zhang Y, Fan S, Hu X, Huang Y, Lu Z, Liu Y (2022) Multi-task short-term reactive and active load forecasting method based on attention-LSTM model. Int J Electric Power Energy Syst 135:107517

    Article  Google Scholar 

  31. Bashir T, Haoyong C, Tahir MF, Liqiang Z (2022) Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN. Energy Rep 8:1678–1686

    Article  Google Scholar 

  32. Müller MR, Gaio G, Carreno EM et al (2020) Electrical load forecasting in disaggregated levels using fuzzy ARTMAP artificial neural network and noise removal by singular spectrum analysis. SN Appl Sci 2:1218

    Article  Google Scholar 

  33. Tavassoli-Hojati Z, Ghaderi SF, Iranmanesh H, Hilber P, Shayesteh E (2020) A self-partitioning local neuro fuzzy model for short-term load forecasting in smart grids. Energy 199:117514

    Article  Google Scholar 

  34. Zhao J, Bose BK (2002) Evaluation of membership functions for fuzzy logic controlled induction motor drive. In: 28th Annual IEEE Conference of the Industrial Electronics Society, Sevilla, Spain

  35. Gupta S, Hussain RR (Reviewing Editor) (2015) Use of triangular membership function for prediction of compressive strength of concrete containing nanosilica. Cogent Eng 2(1)

  36. Ahmad A, Azmira I, Ahmad S, Apandi NIA (2012) Statistical distributions of load profiling data. In: 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia, pp 199–203

  37. Bin Othman MM, Mohamed A, Hussain A (2008) Determination of transmission reliability margin using parametric bootstrap technique. IEEE Trans Power Syst 23(4):1689–1700

    Article  Google Scholar 

  38. Senjyu T, Higa S, Uezato K (1998) Future load curve shaping based on similarity using fuzzy logic approach. IEE Proc Gen Trans Distrib 145(4):375–380

    Article  Google Scholar 

  39. http://traces.cs.umass.edu/index.php/Smart/Smart

  40. Barker S, Mishra A, Irwin D, Cecchet E, Shenoy P, Albrecht J (2012) An open data set and tools for enabling research in sustainable homes. In: Proceedings of the 2012 Workshop on Data Mining Applications in Sustainability (SustKDD 2012), Beijing, China

  41. Makonin S, Ellert B, Bajic IV, Popowich F (2016) Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Sci Data 3(160037):1–12

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Maryam Imani

Authors
  1. Maryam Imani
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Maryam Imani.

Ethics declarations

Conflict of interest

There is no conflict of interest.

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

Imani, M. Fuzzy-based weighting long short-term memory network for demand forecasting. J Supercomput 79, 435–460 (2023). https://doi.org/10.1007/s11227-022-04659-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04659-1

Keywords