Skip to main content

Advertisement

SpringerLink
Log in

Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system

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

Abstract

This paper provides a new hybrid intelligent method for short-term prediction of the market-clearing price of electricity in the presence of wind power plants. The proposed method uses a data filtering technique based on wavelet transform and a radial basis function neural network, which is utilized for primary prediction. The main prediction engine comprises three MLP neural networks with different learning algorithms. To get rid of local minimums and to optimize the all neural networks, the meta-heuristic Imperialist Competitive Algorithm method is used. The input data for network training belong to the Nord Pool power market. The information includes a complete set of the historical record on electricity price and wind power generation. Moreover, the simultaneous impact of wind power generation is analyzed to predict the market-clearing price. Besides, the correlation coefficient factor is provided to consider the impact of wind power in forecasting the electricity price. Simulation results show the supremacy of the proposed method over other methods, to which it has been compared in this study. Also, the prediction error decreases significantly.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Osório G, Matias J, Catalão J (2014) Electricity prices forecasting by a hybrid evolutionary-adaptive methodology. Energy Convers Manag 80:363–373

    Article  Google Scholar 

  2. Shahidehpour M, Yamin H, Li Z (2002) Market operations in electric power systems. IEEE. Wiley-Interscience, Wiley, New York

    Book  Google Scholar 

  3. Shafiee S, Zamani-Dehkordi P, Zareipour H, Knight AM (2016) Economic assessment of a price-maker energy storage facility in the Alberta electricity market. Energy 111:537–547

    Article  Google Scholar 

  4. Nogales FJ, Contreras J, Conejo AJ, Espínola R (2002) Forecasting next-day electricity prices by time series models. IEEE Trans Power Syst 17(2):342–348

    Article  Google Scholar 

  5. Bourbonnais R, Meritet S (2007) Electricity spot price modelling: univariate time series approach. In: The econometrics of energy systems. Springer, pp 51–74

  6. Dukpa A, Duggal I, Venkatesh B, Chang L-Y (2010) Optimal participation and risk mitigation of wind generators in an electricity market. Renew Power Gener IET 4(2):165–175

    Article  Google Scholar 

  7. Vahidinasab V, Jadid S (2010) Bayesian neural network model to predict day-ahead electricity prices. Eur Trans Electr Power 20(2):231–246

    Google Scholar 

  8. Aggarwal SK, Saini LM, Kumar A (2009) Electricity price forecasting in deregulated markets: a review and evaluation. Int J Electr Power Energy Syst 31(1):13–22

    Article  Google Scholar 

  9. Sandhu HS, Fang L, Guan L (2016) Forecasting day-ahead price spikes for the Ontario electricity market. Electr Power Syst Res 141:450–459

    Article  Google Scholar 

  10. Catalao J, Pousinho H, Mendes V (2011) Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Trans Power Syst 26(1):137–144

    Article  Google Scholar 

  11. Bigdeli N, Afshar K, Gazafroudi AS, Ramandi MY (2013) A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada. Renew Sustain Energy Rev 27:20–29

    Article  Google Scholar 

  12. Montoya FG, Manzano-Agugliaro F, López-Márquez S, Hernández-Escobedo Q, Gil C (2014) Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms. Expert Syst Appl 41(15):6585–6595

    Article  Google Scholar 

  13. Manzano-Agugliaro F, Alcayde A, Montoya F, Zapata-Sierra A, Gil C (2013) Scientific production of renewable energies worldwide: an overview. Renew Sustain Energy Rev 18:134–143

    Article  Google Scholar 

  14. Fan S, Liao JR, Yokoyama R, Chen L, Lee W-J (2009) Forecasting the wind generation using a two-stage network based on meteorological information. IEEE Trans Energy Convers 24(2):474–482

    Article  Google Scholar 

  15. Hernandez-Escobedo Q, Manzano-Agugliaro F, Gazquez-Parra JA, Zapata-Sierra A (2011) Is the wind a periodical phenomenon? The case of Mexico. Renew Sustain Energy Rev 15(1):721–728

    Article  Google Scholar 

  16. Hu P, Karki R, Billinton R (2009) Reliability evaluation of generating systems containing wind power and energy storage. IET Gener Transm Distrib 3(8):783–791

    Article  Google Scholar 

  17. Arjmand R, Rahimiyan M (2016) Impact of spatio-temporal correlation of wind production on clearing outcomes of a competitive pool market. Renew Energy 86:216–227

    Article  Google Scholar 

  18. Exizidis L, Kazempour SJ, Pinson P, de Greve Z, Vallée F (2016) Sharing wind power forecasts in electricity markets: a numerical analysis. Appl Energy 176:65–73

    Article  Google Scholar 

  19. MacCormack J, Hollis A, Zareipour H, Rosehart W (2010) The large-scale integration of wind generation: impacts on price, reliability and dispatchable conventional suppliers. Energy Policy 38(7):3837–3846

    Article  Google Scholar 

  20. Kabouris J, Kanellos F (2010) Impacts of large-scale wind penetration on designing and operation of electric power systems. IEEE Trans Sustain Energy 1(2):107–114

    Article  Google Scholar 

  21. Negnevitsky M, Johnson P, Santoso S. Short term wind power forecasting using hybrid intelligent systems. In: 2007 IEEE Power Engineering Society General Meeting

  22. Catalão J, Pousinho H, Mendes V (2011) Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach. Energy Convers Manag 52(2):1061–1065

    Article  Google Scholar 

  23. Abedinia O, Amjady N, Shafie-Khah M, Catalão J (2015) Electricity price forecast using combinatorial neural network trained by a new stochastic search method. Energy Convers Manag 105:642–654

    Article  Google Scholar 

  24. Kaur A, Nonnenmacher L, Pedro HT, Coimbra CF (2016) Benefits of solar forecasting for energy imbalance markets. Renew Energy 86:819–830

    Article  Google Scholar 

  25. Jin CH, Pok G, Lee Y, Park H-W, Kim KD, Yun U, Ryu KH (2015) A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting. Energy Convers Manag 90:84–92

    Article  Google Scholar 

  26. Anbazhagan S, Kumarappan N (2014) Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT. Energy Convers Manag 78:711–719

    Article  Google Scholar 

  27. Voronin S, Partanen J, Kauranne T (2014) A hybrid electricity price forecasting model for the Nordic electricity spot market. Int Trans Electr Energy Syst 24(5):736–760

    Article  Google Scholar 

  28. Amjady N, Keynia F (2009) Day-ahead price forecasting of electricity markets by mutual information technique and cascaded neuro-evolutionary algorithm. IEEE Trans Power Syst 24(1):306–318

    Article  Google Scholar 

  29. Azizi N, Rezakazemi M, Zarei MM (2017) An intelligent approach to predict gas compressibility factor using neural network model. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2979-7

    Article  Google Scholar 

  30. Senjyu T, Toyama H, Areekul P, Chakraborty S, Yona A, Urasaki N, Funabashi T (2009) Next-day peak electricity price forecasting using NN based on rough sets theory. IEEJ Trans Electr Electron Eng 4(5):618–624

    Article  Google Scholar 

  31. Saâdaoui F (2017) A seasonal feedforward neural network to forecast electricity prices. Neural Comput Appl 28(4):835–847

    Article  Google Scholar 

  32. Liu Z, Li W, Sun W (2013) A novel method of short-term load forecasting based on multiwavelet transform and multiple neural networks. Neural Comput Appl 22(2):271–277

    Article  Google Scholar 

  33. Amjady N, Hemmati M (2009) Day-ahead price forecasting of electricity markets by a hybrid intelligent system. Eur Trans Electr Power 19(1):89–102

    Article  Google Scholar 

  34. He Y, Xu Q, Wan J, Yang S (2016) Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function. Energy 114:498–512

    Article  Google Scholar 

  35. Shayeghi H, Ghasemi A, Moradzadeh M, Nooshyar M (2015) Simultaneous day-ahead forecasting of electricity price and load in smart grids. Energy Convers Manag 95:371–384

    Article  Google Scholar 

  36. Hong YY, Liu CY, Chen SJ, Huang WC, Yu TH (2015) Short-term LMP forecasting using an artificial neural network incorporating empirical mode decomposition. Int Trans Electr Energy Syst 25(9):1952–1964

    Article  Google Scholar 

  37. Panapakidis IP, Dagoumas AS (2016) Day-ahead electricity price forecasting via the application of artificial neural network based models. Appl Energy 172:132–151

    Article  Google Scholar 

  38. Jung J, Broadwater RP (2014) Current status and future advances for wind speed and power forecasting. Renew Sustain Energy Rev 31:762–777

    Article  Google Scholar 

  39. González AM, Roque AMS, García-González J (2005) Modeling and forecasting electricity prices with input/output hidden Markov models. IEEE Trans Power Syst 20(1):13–24

    Article  Google Scholar 

  40. Rodriguez CP, Anders GJ (2004) Energy price forecasting in the Ontario competitive power system market. IEEE Trans Power Syst 19(1):366–374

    Article  Google Scholar 

  41. Turner AJ, Miller JF (2014) NeuroEvolution: evolving heterogeneous artificial neural networks. Evol Intell 7(3):135–154

    Article  Google Scholar 

  42. Koppejan R, Whiteson S (2011) Neuroevolutionary reinforcement learning for generalized control of simulated helicopters. Evol Intell 4(4):219–241

    Article  Google Scholar 

  43. Soman SS, Zareipour H, Malik O, Mandal P (2010) A review of wind power and wind speed forecasting methods with different time horizons. In: North American power symposium (NAPS), 2010. IEEE, pp 1–8

  44. Hibon M, Evgeniou T (2005) To combine or not to combine: selecting among forecasts and their combinations. Int J Forecast 21(1):15–24

    Article  Google Scholar 

  45. Abedinia O, Amjady N (2015) Day-ahead price forecasting of electricity markets by a new hybrid forecast method. Model Simul Electr Electron Eng 1(1):1–7

    Google Scholar 

  46. Tripathi M, Upadhyay K, Singh S (2014) Electricity price forecasting using generalized regression neural network (GRNN) for PJM electricity market. Int J Manag Theory Appl (IREMAN) 2(4):137–142

    Google Scholar 

  47. Kakhki IN, Taherian H, Aghaebrahimi MR (2013) Short-term price forecasting under high penetration of wind generation units in smart grid environment. In: Computer and knowledge engineering (ICCKE), 2013 3th international econference on. IEEE, pp 158–163

  48. Amjady N, Keynia F, Zareipour H (2011) Wind power prediction by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization. IEEE Trans Sustain Energy 2(3):265–276

    Article  Google Scholar 

  49. Aghajani A, Kazemzadeh R, Ebrahimi A (2016) A novel hybrid approach for predicting wind farm power production based on wavelet transform, hybrid neural networks and imperialist competitive algorithm. Energy Convers Manag 121:232–240

    Article  Google Scholar 

  50. Yongjun W, Gang Y, Yanying H (2015) Research on Correlation analysis of industry electricity quantity. International Conference on Information Technology and Management Innovation (ICITMI 2015), pp 906–912

  51. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary computation, 2007. CEC 2007. IEEE Congress on. IEEE, pp 4661–4667

  52. Abdechiri M, Faez K, Bahrami H (2010) Neural network learning based on chaotic imperialist competitive algorithm. In: Intelligent systems and applications (ISA), 2010 2nd international workshop on. IEEE, pp 1–5

  53. Niknam T, Fard ET, Pourjafarian N, Rousta A (2011) An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. Eng Appl Artif Intell 24(2):306–317

    Article  Google Scholar 

  54. Jolai F, Sangari MS, Babaie M (2010) Pareto simulated annealing and colonial competitive algorithm to solve an offline scheduling problem with rejection. Proc Inst Mech Eng Part B J Eng Manuf 224(7):1119–1131

    Article  Google Scholar 

  55. Lucas C, Nasiri-Gheidari Z, Tootoonchian F (2010) Application of an imperialist competitive algorithm to the design of a linear induction motor. Energy Convers Manag 51(7):1407–1411

    Article  Google Scholar 

  56. Mahmoudi MT, Forouzideh N, Lucas C, Taghiyareh F (2009) Artificial neural network weights optimization based on imperialist competitive algorithm. In: 7th international conference on computer science and information technologies (CSIT’09), Yerevan. pp 244–247

  57. Li S, Wang P, Goel L (2015) Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electr Power Syst Res 122:96–103

    Article  Google Scholar 

  58. Conejo AJ, Plazas M, Espinola R, Molina AB (2005) Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans Power Syst 20(2):1035–1042

    Article  Google Scholar 

  59. Amjady N, Daraeepour A, Keynia F (2010) Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network. IET Gener Transm Distrib 4(3):432–444

    Article  Google Scholar 

  60. Amjady N, Keynia F (2009) Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy 34(1):46–57

    Article  Google Scholar 

  61. Catalao J, Pousinho H, Mendes V (2011) Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal. IEEE Trans Sustain Energy 2(1):50–59

    Google Scholar 

  62. Mandal P, Zareipour H, Rosehart WD (2014) Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNs. Int J Energy Res 38(13):1654–1666

    Article  Google Scholar 

  63. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  MATH  Google Scholar 

  64. Lei C, Ran L (2008) Short-term wind speed forecasting model for wind farm based on wavelet decomposition. In: Electric utility deregulation and restructuring and power technologies, 2008. DRPT 2008. Third international conference on. IEEE, pp 2525–2529

  65. Pandey AS, Singh D, Sinha SK (2010) Intelligent hybrid wavelet models for short-term load forecasting. IEEE Trans Power Syst 25(3):1266–1273

    Article  Google Scholar 

  66. Chuanan Y, Yongchang Y (2011) A hybrid model to forecast wind speed based on wavelet and neural network. In: Control, automation and systems engineering (CASE), 2011 international conference on. IEEE, pp 1–4

  67. Damousis IG, Alexiadis MC, Theocharis JB, Dokopoulos PS (2004) A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Trans Energy Convers 19(2):352–361

    Article  Google Scholar 

  68. Haque AU, Mandal P, Meng J, Srivastava AK, Tseng T-L, Senjyu T (2013) A novel hybrid approach based on wavelet transform and fuzzy ARTMAP networks for predicting wind farm power production. IEEE Trans Ind Appl 49(5):2253–2261

    Article  Google Scholar 

  69. Shaker H, Zareipour H, Wood D (2013) On error measures in wind forecasting evaluations. In: Electrical and computer engineering (CCECE), 2013 26th annual IEEE Canadian conference on. IEEE, pp 1–6

  70. Madsen H, Pinson P, Kariniotakis G, Nielsen HA, Nielsen T (2005) Standardizing the performance evaluation of shortterm wind power prediction models. Wind Eng 29(6):475–489

    Article  Google Scholar 

  71. Nord Pool market. Available online at the following website: http://www.nordpoolspot.com/

  72. Camastra F, Filippone M (2009) A comparative evaluation of nonlinear dynamics methods for time series prediction. Neural Comput Appl 18(8):1021

    Article  Google Scholar 

  73. Camastra F, Colla AM (1999) Neural short-term prediction based on dynamics reconstruction. Neural Process Lett 9(1):45–52

    Article  Google Scholar 

  74. Amjady N (2006) Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Trans Power Syst 21(2):887–896. https://doi.org/10.1109/TPWRS.2006.873409

    Article  MathSciNet  Google Scholar 

  75. Taherian H, Nazer-Kakhki I, Aghaebrahimi MR, Farshad M, Goldani SR (2014) Short term price forecasting in electricity market considering the effect of wind units’ generation. Comput Intell Electr Eng 5(1):105–122

    Google Scholar 

  76. Mathworks. URL http://www.mathworks.com

Download references

Author information

Authors and Affiliations

  1. Renewable Energy Research Center, Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran

    Afshin Aghajani, Rasool Kazemzadeh & Afshin Ebrahimi

Authors
  1. Afshin Aghajani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rasool Kazemzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Afshin Ebrahimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rasool Kazemzadeh.

Ethics declarations

Conflict of interest

The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Appendix: Terms and definitions

Appendix: Terms and definitions

Here, all terms mentioned in this paper and their definitions are listed in alphabetical order:

A:

Approximation

ANFIS:

Adaptive network-based fuzzy inference system

AR:

Auto-regressive

ARIMA:

Auto-regressive integrated moving average

BFGS:

Broyden–Fletcher–Goldfarb–Shanno

BPNN:

Back propagation neural network

BR:

Bayesian regularization

CNEA:

Cascaded neuro-evolutionary algorithms

CWT:

Continuous wavelet transform

D:

Detail

db4:

Daubechies of order 4

DKK:

Danish krones

DWT:

Discrete wavelet transform

FNN:

Fuzzy neural networks

GARCH:

Generalized auto-regressive conditional heteroskedastic

HPF:

High pass filter

HNN:

Hybrid neural network

HIS:

Hybrid intelligent system

ICA:

Imperialist competitive algorithm

LPF:

low pass filter

LM:

Levenberg–Marquardt

MAPE:

Mean absolute percentage error

MCP:

Market-clearing price

MLP:

Multilayer perceptron

NN:

Neural network

PSO:

Particle swarm optimization

r :

correlation coefficient

RBF:

Radial basis function

SAR:

Seasonal auto-regressive neural network

SCM:

Soft computing model

SSA:

Singular spectrum analysis

WPF:

Wind power forecasting

WT:

Wavelet transform

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aghajani, A., Kazemzadeh, R. & Ebrahimi, A. Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system. Neural Comput & Applic 31, 6981–6993 (2019). https://doi.org/10.1007/s00521-018-3544-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3544-8

Keywords

Search

Navigation