Abstract
In general, electricity prices reflect the cost to build, finance, maintain, and operate power plants and the electricity grid. Therefore, the cost-optimized scheduling of industrial loads with accurate price forecasts is very important. As such, recent studies have attempted to combine models to forecast electricity prices more accurately. Earlier combined models have tended to ignore the selection of sub-models and data analyses, leading to poor forecasting performance. In order to select the best forecasting models in a combined model, we propose a hybrid electricity price forecasting system that includes a data analysis module, a sub-model selection strategy module, optimized forecasting processing, and a model evaluation module. As such, the hybrid system fully exploits the advantages of a single model, thus improving the forecasting performance of the combined model. The experimental results show that the proposed system selects optimal sub-models effectively and successfully identifies future trend changes in the electricity price. Thus, the system can be an effective tool in the planning and implementation of smart grids.
Similar content being viewed by others
References
Agarwal A, Ojha A, Tewari SC, Tripathi MM (2014) Hourly load and price forecasting using ANN and fourier analysis. In: 2014 6th IEEE power India international conference (PIICON). IEEE, pp 1–6
Aggarwal SK, Saini LM, Kumar A (2008) Electricity price forecasting in Ontario electricity market using wavelet transform in artificial neural network based model. Int J Control Autom Syst 6(5):639–650
Aghajani A, Kazemzadeh R, Ebrahimi A (2018) Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3544-8
Alvarez FM, Troncoso A, Riquelme JC, Ruiz JSA (2011) Energy time series forecasting based on pattern sequence similarity. IEEE Trans Knowl Data Eng 23(8):1230–1243
Amjady N (2006) Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Trans Power Syst 21(2):887–896
Amjady N, Keynia F (2010a) Application of a new hybrid neuro-evolutionary system for day-ahead price forecasting of electricity markets. Appl Soft Comput 10(3):784–792
Amjady N, Keynia F (2010b) Electricity market price spike analysis by a hybrid data model and feature selection technique. Electr Power Syst Res 80(3):318–327
Anbazhagan S, Kumarappan N (2013) Day-ahead deregulated electricity market price forecasting using recurrent neural network. IEEE Syst J 7(4):866–872
Bates JM, Granger CWJ (2001) The combination of forecasts. In: Essays in econometrics. Cambridge University Press, Cambridge, UK, pp 451–468
Bashari M, Darudi A, Raeyatdoost N (2014) Kalman fusion algorithm in electricity price forecasting. In: 2014 14th International conference on environment and electrical engineering. IEEE, pp 313–317
Bompard E, Ciwei G, Napoli R, Torelli F (2007) Dynamic price forecast in a competitive electricity market. IET Gener Transm Distrib 1(5):776–783
Cadenas E, Rivera W (2009) Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renew Energy 34(1):274–278
Catalão JPDS, Pousinho HMI, Mendes VMF (2011) Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach. Energy Convers Manag 52(2):1061–1065
Chaâbane N (2014) A hybrid ARFIMA and neural network model for electricity price prediction. Int J Electr Power Energy Syst 55:187–194
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Darudi A, Bashari M, Javidi MH (2015) Electricity price forecasting using a new data fusion algorithm. IET Gener Transm Distrib 9(12):1382–1390
Daubechies I (1992) Ten lectures on wavelets, vol 61. SIAM, Philadelphia
Egrioglu E, Aldag CH, Günay S (2008) A new model selection strategy in artificial neural networks. Appl Math Comput 195:591–597
Elattar EE (2013) Day-ahead price forecasting of electricity markets based on local informative vector machine. IET Gener Transm Distrib 7(10):1063–1071
Fan S, Mao C, Chen L (2007) Next-day electricity-price forecasting using a hybrid network. IET Gener Transm Distrib 1(1):176–182
Gareta R, Romeo LM, Gil A (2006) Forecasting of electricity prices with neural networks. Energy Convers Manag 47(13–14):1770–1778
Geweke J, Amisano G (2011) Optimal prediction pools. J Econ 164(1):130–141
Haque AU, Mandal P, Kaye ME, Meng J, Chang L, Senjyu T (2012) A new strategy for predicting short-term wind speed using soft computing models. Renew Sustain Energy Rev 16(7):4563–4573
Hassan S, Khosravi A, Jaafar J, Raza MQ (2014) Electricity load and price forecasting with influential factors in a deregulated power industry. In: 2014 9th International conference on system of systems engineering (SOSE). IEEE, pp 79–84
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Jiang P, Liu F, Song Y (2016) A hybrid multi-step model for forecasting day-ahead electricity price based on optimization, fuzzy logic and model selection. Energies 9(8):618
Jin CH, Pok G, Lee Y, Park HW, Kim KD, Yun U, Ryu KH (2015a) 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
Jin CH, Pok G, Paik I, Ryu KH (2015b) Short-term electricity load and price forecasting based on clustering and next symbol prediction. IEEJ Trans Electr Electron Eng 10(2):175–180
Jing G, Du W, Guo Y (2012) Studies on prediction of separation percent in electrodialysis process via BP neural networks and improved BP algorithms. Desalination 291:78–93
Kim MK (2015) Short-term price forecasting of Nordic power market by combination Levenberg–Marquardt and Cuckoo search algorithms. IET Gener Transm Distrib 9(13):1553–1563
Kim CI, Yu IK, Song YH (2002) Kohonen neural network and wavelet transform based approach to short-term load forecasting. Electr Power Syst Res 63(3):169–176
Lei M, Shiyan L, Chuanwen J, Hongling L, Yan Z (2009) A review on the forecasting of wind speed and generated power. Renew Sustain Energy Rev 13(4):915–920
Li XR, Yu CW, Ren SY, Chiu CH, Meng K (2013) Day-ahead electricity price forecasting based on panel cointegration and particle filter. Electr Power Syst Res 95:66–76
Lin WM, Gow HJ, Tsai MT (2010a) An enhanced radial basis function network for short-term electricity price forecasting. Appl Energy 87(10):3226–3234
Lin WM, Gow HJ, Tsai MT (2010b) Electricity price forecasting using enhanced probability neural network. Energy Convers Manag 51(12):2707–2714
Liu H, Shi J (2013) Applying ARMA–GARCH approaches to forecasting short-term electricity prices. Energy Econ 37:152–166
Ma Z, Zhong H, Xie L, Xia Q, Kang C (2018) Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model: an ERCOT case study. J Mod Power Syst Clean Energy 6:281–291
Mark MW, Stock JH (2004) Combination forecasts of output growth in a seven country data set. J Forecasting 23(6):405–430
Nagi J, Yap KS, Nagi F, Tiong SK, Ahmed SK (2011) A computational intelligence scheme for the prediction of the daily peak load. Appl Soft Comput 11(8):4773–4788
Niu D, Liu D, Wu DD (2010) A soft computing system for day-ahead electricity price forecasting. Appl Soft Comput 10(3):868–875
Osório GJ, Matias JC, Catalão JP (2014) Hybrid evolutionary-adaptive approach to predict electricity prices and wind power in the short-term. In: 2014 Power systems computation conference. IEEE, pp 1–7
Panapakidis IP, Dagoumas AS (2016) Day-ahead electricity price forecasting via the application of artificial neural network based models. Appl Energy 172:132–151
Priddy KL, Keller PE (2005) Artificial neural networks: an introduction, vol 68. SPIE Press, Bellingham
Rani HJ, Victoire TAA (2019) A hybrid Elman recurrent neural network, group search optimization, and refined VMD-based framework for multi-step ahead electricity price forecasting. Soft Comput 23:8413. https://doi.org/10.1007/s00500-019-04161-6
Safari MIKM, Dahlan NY, Razali NS, Rahman TKA (2013) Electricity prices forecasting using ANN hybrid with invasive weed optimization (IWO). In: 2013 IEEE 3rd international conference on system engineering and technology. IEEE, pp 275–280
Sahay KB (2015) One hour ahead price forecast of Ontario electricity market by using ANN. In: 2015 International conference on energy economics and environment (ICEEE). IEEE, pp 1–6
Sandhu HS, Fang L, Guan L (2014) Forecasting day-ahead electricity prices using data mining and neural network techniques. In: 2014 11th International conference on service systems and service management (ICSSSM). IEEE, pp 1–6
Sarada K, Bapiraju V (2014) Comparison of day-ahead price forecasting in energy market using Neural Network and Genetic Algorithm. In: 2014 International conference on smart electric grid (ISEG). IEEE, pp 1–5
Schwenker F, Kestler HA, Palm G (2001) Three learning phases for radial-basis-function networks. Neural Netw 14(4–5):439–458
Shafie-Khah M, Moghaddam MP, Sheikh-El-Eslami MK (2011) Price forecasting of day-ahead electricity markets using a hybrid forecast method. Energy Convers Manag 52(5):2165–2169
Sharma V, Srinivasan D (2013) A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price prediction in deregulated electricity market. Eng Appl Artif Intel 26(5–6):1562–1574
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
Shih SY, Sun FK, Lee H (2019) Temporal pattern attention for multivariate time series forecasting. Mach Learn 108:1421. https://doi.org/10.1007/s10994-019-05815-0
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192
Wakashiro Y (2019) Estimating price elasticity of demand for electricity: the case of Japanese manufacturing industry. Int J Econ Policy Stud 13(1):173–191
Wan C, Xu Z, Wang Y, Dong ZY, Wong KP (2014) A hybrid approach for probabilistic forecasting of electricity price. IEEE Trans Smart Grid 5(1):463–470
Wang Z, Liu F, Wu J, Wang J (2014) A hybrid forecasting model based on bivariate division and a backpropagation artificial neural network optimized by chaos particle swarm optimization for day-ahead electricity price. Abstrac Appl Anal. https://doi.org/10.1155/2014/249208
Wang J, Liu F, Song Y, Zhao J (2016) A novel model: dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. Appl Soft Comput 48:281–297
Wu W, Zhou J, Mo L, Zhu C (2006) Forecasting electricity market price spikes based on bayesian expert with support vector machines. In: International conference on advanced data mining and applications. Springer, Berlin, pp 205–212
Xiao L, Wang J, Hou R, Wu J (2015) A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. Energy 82:524–549
Yan X, Chowdhury NA (2013) Mid-term electricity market clearing price forecasting: a hybrid LSSVM and ARMAX approach. Int J Electr Power Energy Syst 53:20–26
Yang L, Lv R, Yang Z (2008) Optimizing quality of service of DRM single frequency network. In: 2008 4th IEEE international conference on circuits and systems for communications. IEEE, pp 450–454
Yang W, Wang K, Zuo W (2012) Neighborhood component feature selection for high-dimensional data. JCP 7(1):161–168
Yan-Gao C, Guangwen M (2009) Electricity price forecasting based on support vector machine trained by genetic algorithm. In: 2009 Third international symposium on intelligent information technology application, vol 2. IEEE, pp 292–295
Yun Z, Quan Z, Caixin S, Shaolan L, Yuming L, Yang S (2008) RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans Power Syst 23(3):853–858
Zhang L, Luh PB (2005) Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method. IEEE Trans Power Syst 20(1):59–66
Zhao JH, Dong ZY, Li X (2007) Electricity market price spike forecasting and decision making. IET Gener Transm Distrib 1(4):647–654
Acknowledgements
This work was supported by the Western Project of the National Social Science Foundation of China (Grant No. 18XTJ003).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Fu, T., Zhang, S. & Wang, C. Application and research for electricity price forecasting system based on multi-objective optimization and sub-models selection strategy. Soft Comput 24, 15611–15637 (2020). https://doi.org/10.1007/s00500-020-04888-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-04888-7