Skip to main content

Advertisement

SpringerLink
Log in

Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Electricity demand forecasting plays a crucial role in the operation of electrical power systems because it can provide management decisions related to load switching and power grid. Thus, there have been models developed to estimate the electricity demand. However, inaccurate demand forecasting may raise the operating cost of electric power sector, which means that it would waste considerable money. In this paper, a novel modeling framework was proposed for forecasting electricity demand. Sample entropy was developed to identify the nonlinearity and uncertainty in the original time series, after that redundant noise was removed through a decomposition technique. Besides, the most optimal modes of original series and the optimal input form of the model were determined by the feature selection method. Finally, electricity demand series can be conducted forecasting through least squares support vector machine tuned by multi-objective sine cosine optimization algorithm. The case studies of Australia demonstrated that the proposed framework can ensure high accuracy and strong stability. Thus, it can be considered as a useful tool for electricity demand forecasting.

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

Similar content being viewed by others

Abbreviations

MBE:

Mean bias error

QLD:

Queensland

SE:

Sample entropy

SA:

South Australia

NSW:

New South Wales

VMs:

Variational modes

MAE:

Mean absolute error

SCA:

Sine–cosine algorithm

PSR:

Phase space reconstruction

SVM:

Support vector machine

ANN:

Artificial neural network

LSSVM:

Least squares support vector machine

ADMM:

Alternate direction method of multipliers

ARIMA:

Autoregressive integrated moving average

SARIMA:

Multi-objective sine cosine algorithm

VMD:

Variational mode decomposition

SE:

Sample entropy

VM:

Variational modes

DWT:

Discrete wavelet transform

NWP:

Numerical weather prediction

EMD:

Ensemble mode decomposition

SVR:

Support vector regression

THI:

Theil inequality coefficient

MAPE:

Mean absolute percentage error

PACF:

Partial autocorrelation function

VMD:

Variational mode decomposition

BPNN:

Backpropagation neural network

References

  1. Fan S, Chen LN (2006) Short-term load forecasting based on an adaptive hybrid method. IEEE Trans Power Syst 21(1):392–401

    MathSciNet  Google Scholar 

  2. Ying LC, Pan MC (2008) Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers Manag 49(2):205–211

    MathSciNet  Google Scholar 

  3. Wang X, Yang LT, Liu H, Deen MJ (2018) A big data-as-a-service framework: state-of-the-art and perspectives. IEEE Trans Big Data 4(3):325–340

    Google Scholar 

  4. Meadea N, Islam T (2015) Modelling European usage of renewable energy technologies for electricity generation. Technol Forecast Soc Change 90(Part B):497–509

    Google Scholar 

  5. Song M, Du Q, Zhu Q (2017) A theoretical method of environmental performance evaluation in the context of big data. Prod Plan Control 28(11–12):976–984

    Google Scholar 

  6. Saab S, Badr E, Nasr G (2001) Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon. Energy 26(1):1–14

    Google Scholar 

  7. De Felice M, Alessandri A, Ruti PM (2013) Electricity demand forecasting over Italy: potential benefits using numerical weather prediction models. Electr Power Syst Res 104:71–79

    Google Scholar 

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

    Google Scholar 

  9. Vu DH, Muttaqi KM, Agalgaonkar AP, Bouzerdoum A (2017) Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment. Appl Energy 205:790–801

    Google Scholar 

  10. Yang Y, Chen YH, Wang YC, Li CH, Li L (2016) Modelling a combined method based on ANFIS and neural network improved by DE algorithm: a case study for short-term electricity demand forecasting. Appl Soft Comput 49:663–675

    Google Scholar 

  11. Williams S, Short M (2020) Electricity demand forecasting for decentralised energy management. Energy Built Environ 1(2):178–186

    Google Scholar 

  12. Chang PC, Fan CY, Lin JJ (2011) Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach. Int J Electr Power Energy Syst 33(1):17–27

    Google Scholar 

  13. An N, Zhao WG, Wang JZ, Shang D, Zhao ED (2013) Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 49:279–288

    Google Scholar 

  14. Bedi J, Toshniwal D (2019) Deep learning framework to forecast electricity demand. Appl Energy 238:1312–1326

    Google Scholar 

  15. Jiang P, Li RR, Liu NN, Gao YY (2020) A novel composite electricity demand forecasting framework by data processing and optimized support vector machine. Appl Energy 260:114243. https://doi.org/10.1016/j.apenergy.2019.114243

    Article  Google Scholar 

  16. AL-Musaylh MS, Deo RC, Adamowski JF, Li Y (2019) Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia. Renew Sustain Energy Rev 113:109293

    Google Scholar 

  17. Song ZY, Niu DX, Dai SY, Xiao XL, Wang YW (2017) Incorporating the influence of China’s industrial capacity elimination policies in electricity demand forecasting. Util Policy 47:1–11

    Google Scholar 

  18. Ren Y, Suganthan PN, Srikanth N, Amaratunga G (2016) Random vector functional link network for short-term electricity load demand forecasting. Inf Sci 367:1078–1093

    Google Scholar 

  19. Shao Z, Gao F, Yang SL, Yu BG (2015) A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: hidden characteristic extraction and probability density prediction. Renew Sustain Energy Rev 52:876–889

    Google Scholar 

  20. He YX, Jiao J, Chen Q, Ge SF, Chang Y, Xu Y (2017) Urban long term electricity demand forecast method based on system dynamics of the new economic normal: the case of Tianjin. Energy 133:9–22

    Google Scholar 

  21. Laouafi A, Mordjaoui M, Laouafi F, Boukelia TE (2016) Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology. Int J Electr Power Energy Syst 77:136–144

    Google Scholar 

  22. Burillo D, Chester MV, Ruddell B, Johnson N (2017) Electricity demand planning forecasts should consider climate non-stationarity to maintain reserve margins during heat waves. Appl Energy 206:267–277

    Google Scholar 

  23. De Felice M, Alessandri A, Catalano F (2015) Seasonal climate forecasts for medium-term electricity demand forecasting. Appl Energy 137:435–444

    Google Scholar 

  24. El-Shazly A (2013) Electricity demand analysis and forecasting: a panel cointegration approach. Energy Econ 40:251–258

    Google Scholar 

  25. Gunay ME (2016) Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: case of Turkey. Energy Policy 90:92–101

    Google Scholar 

  26. Jiang P, Li R, Lu H et al (2019) Modeling of electricity demand forecast for power system. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04153-5

    Article  Google Scholar 

  27. Shaikh F, Ji Q (2016) Forecasting natural gas demand in China: logistic modelling analysis. Int J Electr Power Energy Syst 77:25–32

    Google Scholar 

  28. Kelo S, Dudul S (2012) A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature. Int J Electr Power Energy Syst 43(1):1063–1071

    Google Scholar 

  29. Ghofrani M, Ghayekhloo M, Arabali A, Ghayekhloo A (2015) A hybrid short-term load forecasting with a new input selection framework. Energy 81:777–786

    Google Scholar 

  30. Singh P, Dwivedi P (2018) Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem. Appl Energy 217:537–549

    Google Scholar 

  31. Khwaja AS, Zhang X, Anpalagan A, Venkatesh B (2017) Boosted neural networks for improved short-term electric load forecasting. Electr Power Syst Res 143:431–437

    Google Scholar 

  32. Karimi M, Karami H, Gholami M, Khatibzadehazad H, Moslemi N (2018) Priority index considering temperature and date proximity for selection of similar days in knowledge-based short term load forecasting method. Energy 144:928–940

    Google Scholar 

  33. Fang K, Jiang Y, Song M (2016) Customer profitability forecasting using Big Data analytics: a case study of the insurance industry. Comput Ind Eng 101:554–564

    Google Scholar 

  34. Efendi R, Ismail Z, Deris MM (2015) A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand. Appl Soft Comput 28:422–430

    Google Scholar 

  35. Ju FY, Hong WC (2013) Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting. Appl Math Model 37(23):9643–9651

    MathSciNet  MATH  Google Scholar 

  36. Kandananond K (2011) Forecasting electricity demand in Thailand with an artificial neural network approach. Energies 4(8):1246–1257

    Google Scholar 

  37. Hassan S, Khosravi A, Jaafar J, Khanesar MA (2016) A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting. Int J Electr Power Energy Syst 82:1–10

    Google Scholar 

  38. Kaytez F, Taplamacioglu MC, Cam E, Hardalac F (2015) Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int J Electr Power Energy Syst 67:431–438

    Google Scholar 

  39. Chang EH, Zhu GN, Chen JW (2015) A combined model based on cuckoo search algorithm for electrical load forecasting. Appl Mech Mater 737:278–282

    Google Scholar 

  40. Kucukali S, Baris K (2010) Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach. Energy Policy 38(5):2438–2445

    Google Scholar 

  41. Wang DY, Luo HY, Grunder O, Lin YB, Guo HX (2017) Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. Appl Energy 190:390–407

    Google Scholar 

  42. Deo RC, Wen XH, Qi F (2016) A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Appl Energy 168:568–593

    Google Scholar 

  43. Rathinasamy M, Khosa R, Adamowski J, Ch S, Partheepan G, Anand J, Narsimlu B (2014) Wavelet-based multiscale performance analysis: an approach to assess and improve hydrological models. Water Resour Res 50(12):9721–9737

    Google Scholar 

  44. Xiong T, Bao YK, Hu ZY (2014) Interval forecasting of electricity demand: a novel bivariate EMD-based support vector regression modeling framework. Int J Electr Power Energy Syst 63:353–362

    Google Scholar 

  45. Liu H, Tian HQ, Liang XF, Li YF (2015) Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Appl Energy 157:183–194

    Google Scholar 

  46. Qiu XH, Suganthan PN, Amaratunga GAJ (2018) Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting. Knowl Based Syst 145:182–196

    Google Scholar 

  47. Lahmiri S (2016) A variational mode decompoisition approach for analysis and forecasting of economic and financial time series. Expert Syst Appl 55:268–273

    Google Scholar 

  48. Chou CM (2014) Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales. Stoch Environ Res Risk Assess 28(6):1401–1408

    Google Scholar 

  49. Stosic T, Telesca L, Ferreira DVD, Stosic B (2016) Investigating anthropically induced effects in streamflow dynamics by using permutation entropy and statistical complexity analysis: a case study. J Hydrol 540:1136–1145

    Google Scholar 

  50. Zurek S, Guzik P, Pawlak S, Kosmider M, Piskorski J (2012) On the relation between correlation dimension, approximate entropy and sample entropy parameters, and a fast algorithm for their calculation. Physica A 391(24):6601–6610

    Google Scholar 

  51. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049

    Google Scholar 

  52. Li HM, Wang JZ, Li RR, Lu HY (2019) Novel analysis-forecast system based on multi-objective optimization for air quality index. J Clean Prod 208:1365–1383

    Google Scholar 

  53. Wang YK, Tao YW, Sheng D, Zhou YT, Wang D, Shi XR, Wu JC, Ma XR (2020) Quantifying the change in streamflow complexity in the Yangtze River. Environ Res 180:108833

    Google Scholar 

  54. Li RR, Jin Y (2018) A wind speed interval prediction system based on multi-objective optimization for machine learning method. Appl Energy 228:2207–2220

    Google Scholar 

  55. Dragomiretskiy K, Zosso D (2014) Variational Mode Decomposition. IEEE Trans Signal Process 62(3):531–544

    MathSciNet  MATH  Google Scholar 

  56. Mirjalili S (2016) SCA: A sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Google Scholar 

  57. Jiang P, Li RR, Li HM (2019) Multi-objective algorithm for the design of prediction intervals for wind power forecasting model. Appl Math Model 67:101–122

    MathSciNet  MATH  Google Scholar 

  58. Li B, Li DY, Zhang ZJ, Yang SM, Wang F (2015) Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine. Appl Math Model 39(17):5253–5264

    MathSciNet  MATH  Google Scholar 

  59. Zhu BZ, Wei YM (2013) Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology. Omega Int J Manag Sci 41(3):517–524

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Economics and Management, Hebei University of Technology, Tianjin, 300401, China

    Ranran Li

  2. Chinese Academy of Social Sciences, Beijing, 100732, China

    Xueli Chen

  3. Lithuanian Institute of Agrarian Economics, Vilnius, Lithuania

    Tomas Balezentis & Dalia Streimikiene

  4. Shanghai University of Finance and Economics, Shanghai, 200433, China

    Zhiyong Niu

Authors
  1. Ranran Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xueli Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tomas Balezentis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dalia Streimikiene
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiyong Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiyong Niu.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Li, R., Chen, X., Balezentis, T. et al. Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application. Neural Comput & Applic 33, 301–320 (2021). https://doi.org/10.1007/s00521-020-04996-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04996-3

Keywords

Search

Navigation