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

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## 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.

## Keywords

Neural networks Imperialist competitive algorithm Power system market Price forecasting## Notes

### Compliance with ethical standards

#### 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.

## References

- 1.Osório G, Matias J, Catalão J (2014) Electricity prices forecasting by a hybrid evolutionary-adaptive methodology. Energy Convers Manag 80:363–373CrossRefGoogle Scholar
- 2.Shahidehpour M, Yamin H, Li Z (2002) Market operations in electric power systems. IEEE. Wiley-Interscience, Wiley, New YorkCrossRefGoogle 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 111:537–547CrossRefGoogle 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):342–348CrossRefGoogle Scholar
- 5.Bourbonnais R, Meritet S (2007) Electricity spot price modelling: univariate time series approach. In: The econometrics of energy systems. Springer, pp 51–74Google Scholar
- 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):165–175CrossRefGoogle Scholar
- 7.Vahidinasab V, Jadid S (2010) Bayesian neural network model to predict day-ahead electricity prices. Eur Trans Electr Power 20(2):231–246Google 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):13–22CrossRefGoogle Scholar
- 9.Sandhu HS, Fang L, Guan L (2016) Forecasting day-ahead price spikes for the Ontario electricity market. Electr Power Syst Res 141:450–459CrossRefGoogle 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):137–144CrossRefGoogle 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 27:20–29CrossRefGoogle 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):6585–6595CrossRefGoogle 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 18:134–143CrossRefGoogle 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):474–482CrossRefGoogle 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):721–728CrossRefGoogle 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):783–791CrossRefGoogle 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 86:216–227CrossRefGoogle 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 176:65–73CrossRefGoogle 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):3837–3846CrossRefGoogle 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):107–114CrossRefGoogle Scholar
- 21.Negnevitsky M, Johnson P, Santoso S. Short term wind power forecasting using hybrid intelligent systems. In: 2007 IEEE Power Engineering Society General MeetingGoogle Scholar
- 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):1061–1065CrossRefGoogle 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 105:642–654CrossRefGoogle Scholar
- 24.Kaur A, Nonnenmacher L, Pedro HT, Coimbra CF (2016) Benefits of solar forecasting for energy imbalance markets. Renew Energy 86:819–830CrossRefGoogle 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 90:84–92CrossRefGoogle Scholar
- 26.Anbazhagan S, Kumarappan N (2014) Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT. Energy Convers Manag 78:711–719CrossRefGoogle 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):736–760CrossRefGoogle 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):306–318CrossRefGoogle 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 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):618–624CrossRefGoogle Scholar
- 31.Saâdaoui F (2017) A seasonal feedforward neural network to forecast electricity prices. Neural Comput Appl 28(4):835–847CrossRefGoogle 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):271–277CrossRefGoogle 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):89–102CrossRefGoogle 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 114:498–512CrossRefGoogle 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 95:371–384CrossRefGoogle 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):1952–1964CrossRefGoogle Scholar
- 37.Panapakidis IP, Dagoumas AS (2016) Day-ahead electricity price forecasting via the application of artificial neural network based models. Appl Energy 172:132–151CrossRefGoogle Scholar
- 38.Jung J, Broadwater RP (2014) Current status and future advances for wind speed and power forecasting. Renew Sustain Energy Rev 31:762–777CrossRefGoogle 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):13–24CrossRefGoogle Scholar
- 40.Rodriguez CP, Anders GJ (2004) Energy price forecasting in the Ontario competitive power system market. IEEE Trans Power Syst 19(1):366–374CrossRefGoogle Scholar
- 41.Turner AJ, Miller JF (2014) NeuroEvolution: evolving heterogeneous artificial neural networks. Evol Intell 7(3):135–154CrossRefGoogle Scholar
- 42.Koppejan R, Whiteson S (2011) Neuroevolutionary reinforcement learning for generalized control of simulated helicopters. Evol Intell 4(4):219–241CrossRefGoogle 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 1–8Google Scholar
- 44.Hibon M, Evgeniou T (2005) To combine or not to combine: selecting among forecasts and their combinations. Int J Forecast 21(1):15–24CrossRefGoogle 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):1–7Google 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):137–142Google 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 158–163Google Scholar
- 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):265–276CrossRefGoogle 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 121:232–240CrossRefGoogle 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 906–912Google Scholar
- 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 4661–4667Google Scholar
- 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 1–5Google Scholar
- 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):306–317CrossRefGoogle 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):1119–1131CrossRefGoogle 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):1407–1411CrossRefGoogle 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 244–247Google Scholar
- 57.Li S, Wang P, Goel L (2015) Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electr Power Syst Res 122:96–103CrossRefGoogle 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):1035–1042CrossRefGoogle 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):432–444CrossRefGoogle 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):46–57CrossRefGoogle 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):50–59Google 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):1654–1666CrossRefGoogle Scholar
- 63.Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693CrossRefMATHGoogle 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 2525–2529Google Scholar
- 65.Pandey AS, Singh D, Sinha SK (2010) Intelligent hybrid wavelet models for short-term load forecasting. IEEE Trans Power Syst 25(3):1266–1273CrossRefGoogle 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 1–4Google Scholar
- 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):352–361CrossRefGoogle 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):2253–2261CrossRefGoogle 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 1–6Google Scholar
- 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):475–489CrossRefGoogle 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):1021CrossRefGoogle Scholar
- 73.Camastra F, Colla AM (1999) Neural short-term prediction based on dynamics reconstruction. Neural Process Lett 9(1):45–52CrossRefGoogle 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 MathSciNetCrossRefGoogle 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):105–122Google Scholar
- 76.Mathworks. URL http://www.mathworks.com