Skip to main content
SpringerLink
Log in

Applications of empirical wavelet decomposition, statistical feature extraction, and antlion algorithm with support vector regression for resident electricity consumption forecasting

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

Accurate forecasting of residential power load provides key support for energy policymakers and power system managers. However, due to the strong volatility and nonlinearity of residential power load, the accuracy of using traditional algorithms to predict residential power load is not high. Given the volatility and nonlinearity of residential power consumption, this paper proposes a feature extraction and antlion hybrid intelligent power forecasting algorithm, namely EWT-S-ALOSVR. Empirical wavelet decomposition (EWT) can extract the features of multiple factors that affect residential electricity consumption to form multiple sub-columns with characteristics. Disassemble the sequence into multiple fluctuation sources, and analyze the characteristics of the fluctuation sources. Support vector regression (SVR) is a linear expression of nonlinear behavior, which can well solve the nonlinearity of residential power load, and the appropriate model parameters are selected by using antlion optimization (ALO) random walk and elite selection methods. The hybrid of the SVR algorithm and ALO optimization can not only effectively identify the fluctuation and nonlinearity of power load, but also predict more accurately. This paper selects the power load of a certain residential working day for analysis and obtains the correlation between the potential characteristics of residential electricity and the selection of parameters through statistical analysis. The model is optimized according to the relationship between parameters and data characteristics. The optimized model’s prediction outcomes are more accurate and robust when compared to those of other models.

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

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

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

Data availability

The real-world residential electricity consumption data are collected from Umass Smart Data Set with hyper-linkage: https://traces.cs.umass.edu/index.php/Smart/Smart, and the data are employed to implement the experiments.

Abbreviations

\(\hat{\psi }_{{\text{n}}} \left( \omega \right)\) :

Fourier transform of empirical wavelet function

\(\hat{\phi }_{{1}} \left( \omega \right)\) :

Fourier transform of empirical scaling function

N :

The number of load data in the analysis time period

\(X_{n,d}\) :

The location of the ants

t :

Current iteration count

T :

The maximum number of iterations

\(M_{{{\text{Ant}}}}\) :

Matrix for each ant position

\(A_{i,j}\) :

The value of the jth variable of the ith ant

\(M_{{{\text{OA}}}}\) :

A matrix holding the fitness of each ant

\(f\) :

Objective function

\(M_{{{\text{Antlion}}}}\) :

Matrix for each antlion location

\({\text{AL}}_{i,j}\) :

The value of the jth dimension of the ith antlion

\(c^{t}\) :

The minimum value of all variables in the t iteration

\(d^{t}\) :

The maximum value of all variables in the t iteration

\({\text{Antlion}}_{j}^{t}\) :

The position of the jth antlion selected in the tth iteration

\({\text{AOV}}\) :

Maximum amplitude

\({\text{aov}}_{i}\) :

Adjacent fluctuations within a time period

\({\text{RV}}\) :

Rising volatility

\(RV\) :

ITh bottom

\({\text{DT}}_{i}\) :

Time distance between two bottoms

\({\text{DV}}\) :

Declining volatility

\(U_{i}\) :

ITh top

\({\text{UT}}_{i}\) :

Time distance between two tops

\({\text{MFI}}\) :

Average fluctuation interval

\({\text{VP}}\) :

The number of fluctuating points in the forecast area

\({\text{IL}}\) :

Interval length

References

  1. Ji, T., Jiang, Y., Li, M., Wu, Q.: Ultra-short-term wind speed and wind power forecast via selective Hankelization and low-rank tensor learning-based predictor. Int. J. Electr. Power Energy Syst. 140, 107994 (2022). https://doi.org/10.1016/j.ijepes.2022.107994

    Article  Google Scholar 

  2. Xu, F., Shu, C., Shao, J., Xiang, N.: Uncovering urban residents’ electricity conservation and carbon reduction potentials in megacities of China: a systematic path of behavioural interventions. Resour. Conserv. Recycl. 173, 105703 (2021). https://doi.org/10.1016/j.resconrec.2021.105703

    Article  Google Scholar 

  3. Sun, Y., Wang, Z., Zhang, B., Zhao, W., Xu, F., Liu, J., Wang, B.: Residents’ sentiments towards electricity price policy: evidence from text mining in social media. Resour. Conserv. Recycl. 160, 104903 (2020). https://doi.org/10.1016/j.resconrec.2020.104903

    Article  Google Scholar 

  4. Jiang, W., Zeng, B., Yang, Z., Li, G.: Resident load influence analysis method for price based on non-intrusive load monitoring and decomposition data. IOP Conf. Ser. Earth Environ. Sci. 108(5), 052047 (2018). https://doi.org/10.1088/1755-1315/108/5/052047

    Article  Google Scholar 

  5. Zhang, L., Wen, X.: Nonlinear effect analysis of electricity price on household electricity consumption. Math. Probl. Eng. 13, 8503158 (2021). https://doi.org/10.1155/2021/8503158

    Article  Google Scholar 

  6. Caraka, R.E., Bakar, S.A., Tahmid, M.: Rainfall forecasting multi kernel support vector regression seasonal autoregressive integrated moving average (MKSVR-SARIMA). AIP Conf. Proc. 2111(1), 020014 (2019). https://doi.org/10.1063/1.5111221

    Article  Google Scholar 

  7. Ramírez-Mendiola, J.L., Grünewald, P., Eyre, N.: Linking intra-day variations in residential electricity demand loads to consumers’ activities: What’s missing? Energy Build. 161, 63–71 (2018). https://doi.org/10.1016/j.enbuild.2017.12.012

    Article  Google Scholar 

  8. Jin, Z., Guo, K., Sun, Y., Lai, L., Liao, Z.: The industrial asymmetry of the stock price prediction with investor sentiment: based on the comparison of predictive effects with SVR. J. Forecast. 39, 1166–1178 (2020). https://doi.org/10.1002/for.2681

    Article  MathSciNet  Google Scholar 

  9. Li, D., Ma, J., Rao, K., Wang, X., Li, R., Yang, Y., Zheng, H.: Prediction of rainfall time series using the hybrid DWT-SVR-Prophet model. Water 15, 19–35 (2023). https://doi.org/10.3390/w15101935

    Article  Google Scholar 

  10. Zhang, X., Mohanty, S.N., Parida, A.K., Pani, S.K., Dong, B., Cheng, X.: Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha. IEEE Access 8, 30223–30233 (2020). https://doi.org/10.1109/ACCESS.2020.2972435

    Article  Google Scholar 

  11. Vázquez-Patiño, A., Peña, M., Avilés, A.: Assessment of quarterly, semiannual and annual models to forecast monthly rainfall anomalies: the case of a tropical Andean Basin. Atmosphere 13, 895 (2022). https://doi.org/10.3390/ATMOS13060895

    Article  Google Scholar 

  12. Quach, K.N.D., Ren, Z., Tran, K.V., Vu, V.H., Chun, Y., Nguyen, T.T., Jo, J.: Short-term traffic speed prediction using hybrid LSTM-SVR model. Robot Intell. Technol. Appl. 7, 642 (2023). https://doi.org/10.1007/978-3-031-26889-2_40

    Article  Google Scholar 

  13. Zhang, W., Gu, L., Shi, Y., Luo, X., Zhou, H.: A hybrid SVR with the firefly algorithm enhanced by a logarithmic spiral for electric load forecasting. Front. Energy Res. 10, 977854 (2022). https://doi.org/10.3389/fenrg.2022.977854

    Article  Google Scholar 

  14. Ribeiro, A.M.N.C., do Carmo, P.R.X., Rodrigues, I.R., Sadok, D., Lynn, T., Endo, P.T.: Short-term firm-level energy-consumption forecasting for energy-intensive manufacturing: a comparison of machine learning and deep learning models. Algorithms 13(11), 274 (2020). https://doi.org/10.3390/a13110274

    Article  MathSciNet  Google Scholar 

  15. Mounir, N., Ouadi, H.: Short-term electric load forecasting model based on SVR technique. Lect. Notes Netw. Syst. 714, 331–342 (2023). https://doi.org/10.1007/978-3-031-35245-4_30

    Article  Google Scholar 

  16. Wang, R., Xia, X., Li, Y., Cao, W.: Clifford fuzzy support vector machine for regression and its application in electric load forecasting of energy system. Front. Energy Res. 9, 793078 (2021). https://doi.org/10.3389/fenrg.2021.793078

    Article  Google Scholar 

  17. Falayi, E.O., Ogundile, O.O., Adepitan, J.O., Okusanya, A.A.: Solar quiet variation of the horizontal and vertical components of geomagnetic field using wavelet analysis. Can. J. Phys. 97(4), 450–460 (2018). https://doi.org/10.1139/cjp-2018-0034

    Article  Google Scholar 

  18. Raja, M.A.Z., Khan, M.A.R., Mahmood, T., Farooq, U., Chaudhary, N.I.: Design of bio-inspired computing technique for nano-fluidics based on nonlinear Jeffery–Hamel flow equations. Can. J. Phys. 94(5), 474–489 (2016). https://doi.org/10.1139/cjp-2015-0440

    Article  Google Scholar 

  19. Al-Raeei, M.: Applying fractional quantum mechanics to systems with electrical screening effects. Chaos Solitons Fractals 150, 111209 (2021). https://doi.org/10.1016/j.chaos.2021.111209

    Article  MathSciNet  Google Scholar 

  20. Feng, T., Liu, C.S., Xu, A., Wang, C.H., Wang, F.M., Liu, X., Su, S.T.: Research on transformer partial discharge feature extraction based on empirical wavelet transform and multiscale permutation entropy. J. Phys. Conf. Ser. 2492, 012010 (2023). https://doi.org/10.1088/1742-6596/2492/1/012010

    Article  Google Scholar 

  21. Lakshmipriya, B., Jayalakshmy, S.: Wavelet scattering and scalogram visualization based human brain decoding using empirical wavelet transform. Int. J. Inf. Technol. 15, 1699–1708 (2023). https://doi.org/10.1007/s41870-023-01213-x

    Article  Google Scholar 

  22. Mohammadi, H.A., Ghofrani, S., Nikseresht, A.: Using empirical wavelet transform and high-order fuzzy cognitive maps for time series forecasting. Appl. Soft Comput. 135, 109990 (2023). https://doi.org/10.1016/j.asoc.2023.109990

    Article  Google Scholar 

  23. Wang, X., Wang, Y., Mussetta, M.: A hybrid model of EMD and PSO-SVR for short-term load forecasting in residential quarters. Math. Probl. Eng. 2016, 9895639 (2016). https://doi.org/10.1155/2016/9895639

    Article  Google Scholar 

  24. Yao, X., Mao, S.: Electric supply and demand forecasting using seasonal grey model based on PSO-SVR. Grey Syst. Theory Appl. 13, 141–171 (2023). https://doi.org/10.1108/GS-10-2021-0159

    Article  Google Scholar 

  25. Liu, J.P.: Power load combination forecasting based on triangular fuzzy discrete difference equation forecasting model and PSO-SVR. J. Intell. Fuzzy Syst. 36, 5889–5898 (2019). https://doi.org/10.3233/JIFS-181717

    Article  Google Scholar 

  26. Tavakkoli, A., Rezaeenour, J., Hadavandi, E.: A novel forecasting model based on support vector regression and bat meta-heuristic (Bat–SVR): case study in printed circuit board industry. Int. J. Inf. Technol. Decis. Mak. 14(1), 195–215 (2015). https://doi.org/10.1142/S0219622014500849

    Article  Google Scholar 

  27. Zhu, Y., Huang, C., Wang, Y., Wang, J.: Application of bionic algorithm based on CS-SVR and BA-SVR in short-term traffic state prediction modeling of urban road. Int. J. Automot. Technol. 23, 1141–1151 (2022). https://doi.org/10.1007/s12239-022-0100-4

    Article  Google Scholar 

  28. Zheng, J., Wang, Y., Li, S., Chen, H.: The stock index prediction based on SVR model with bat optimization algorithm. Algorithms 14, 299 (2021). https://doi.org/10.3390/A14100299

    Article  Google Scholar 

  29. Pan, W.T., Liu, Y., Jiang, H., Chen, Y.T., Liu, T., Qing, Y., Huang, G.H., Li, R.: Model construction of enterprise financial early warning based on quantum FOA-SVR. Sci. Program. 8, 5018917 (2021). https://doi.org/10.1155/2021/5018917

    Article  Google Scholar 

  30. Fan, J., Hu, Q., Tang, Z.: Predicting vacant parking space availability: an SVR method with fruit fly optimisation. IET Intel. Transp. Syst. 12(10), 1414–1420 (2018). https://doi.org/10.1049/iet-its.2018.5031

    Article  Google Scholar 

  31. Algamal, Z.Y., Qasim, M.K., Lee, M.H., Ali, H.T.M.: Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression. Chemom. Intell. Lab. Syst. 208, 104196 (2021). https://doi.org/10.1016/j.chemolab.2020.104196

    Article  Google Scholar 

  32. Talaat, M., Farahat, M.A., Mansour, N., Hatata, A.Y.: Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach. Energy 196, 117087 (2020). https://doi.org/10.1016/j.energy.2020.117087

    Article  Google Scholar 

  33. Barman, M., Choudhury, N.B.D.: Hybrid GOA-SVR technique for short term load forecasting during periods with substantial weather changes in North-East India. Procedia Comput. Sci. 143, 124–132 (2018). https://doi.org/10.1016/j.procs.2018.10.360

    Article  Google Scholar 

  34. Gilles, J.: Empirical wavelet transform. IEEE Trans. Signal Process. 61(16), 3999–4010 (2013). https://doi.org/10.1109/TSP.2013.2265222

    Article  MathSciNet  MATH  Google Scholar 

  35. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015). https://doi.org/10.1016/j.advengsoft.2015.01.010

    Article  Google Scholar 

  36. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut. Comput. 1, 3–18 (2011). https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

Download references

Funding

This work was supported in part by the Science and Technology of Henan Province of China (No. 182400410419), and the Foundation for Fostering the National Foundation of Pingdingshan University (No. PXY-PYJJ-2016006), and National Science and Technology Council of Taiwan under Grant MOST 111-2410-H-161-001.

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Ping Ding Shan University, Ping Ding Shan, 467000, Henan, China

    Guo-Feng Fan, Ruo-Tong Zhang & Cen-Cen Cao

  2. Department of Information Management, Asia Eastern University of Science and Technology, No. 58, Sec. 2, Sichuan Rd., Panchiao District, New Taipei, 22064, Taiwan

    Yi-Hsuan Yeh & Wei-Chiang Hong

  3. Department of Information Management, Yuan Ze University, New Taipei, 22064, Taiwan

    Wei-Chiang Hong

Authors
  1. Guo-Feng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruo-Tong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cen-Cen Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi-Hsuan Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei-Chiang Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by R.-T.Z., C.-C.C., and Y.-H.Y. The first draft of the manuscript was written by G.-F.F. and W.-C.H., and all authors commented on the versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Wei-Chiang Hong.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, GF., Zhang, RT., Cao, CC. et al. Applications of empirical wavelet decomposition, statistical feature extraction, and antlion algorithm with support vector regression for resident electricity consumption forecasting. Nonlinear Dyn 111, 20139–20163 (2023). https://doi.org/10.1007/s11071-023-08922-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-023-08922-9

Keywords

Search

Navigation