This paper presents a synergy of three methods for training the Elman recurrent neural network to forecast the multi-step-ahead electricity price in an electric power system. Electricity prices are characterized as non-stationary time series data that entail vigorous learning model for predicting the future electricity price from past data. To accomplish this, an enhanced hybrid framework that integrates the refined variational mode decomposition method and the group search optimization algorithm is proposed for training the Elman recurrent neural network. The variational mode decomposition method is optimized using a complement particle swarm optimization method so as to decompose the non-stationary pricing data into optimum number of intrinsic mode functions. Subsequently, based on the power values of intrinsic mode, functions are further filtered and used as the input data to train the Elman neural network. The group search optimization algorithm is used to optimize the weights of the Elman neural network. Three real-time time series non-stationary data for multi-step ahead price prediction are adopted from Australian, British, and Indian power markets and experimented using the proposed forecasting model.
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Abedinia O, Amjady N, Shafie-Khah M, Catalão JPS (2015) Electricity price forecast using combinatorial neural network trained by a new stochastic search method. Energy Convers Manag 105:642–654
Amjady N (2006) Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Trans Power Syst 21(2):887–896
Anbazhagan S, Kumarappan N (2013) Day-ahead deregulated electricity market price forecasting using recurrent neural network. IEEE Syst J 7(4):866–872
Andalib A, Atry F (2009) Multi-step ahead forecasts for electricity prices using NARX: a new approach, a critical analysis of one-step ahead forecasts. Energy Convers Manag 50(3):739–747
Aneiros G, Vilar J, Raña P (2016) Short-term forecast of daily curves of electricity demand and price. Int J Electr Power Energy Syst 80:96–108
Aruldoss Albert Victoire T, Ebenezer Jeyakumar A (2004) Hybrid PSO–SQP for economic dispatch with valve-point effect. Electr Power Syst Res 71(1):51–59
Blanco A, Delgado M, Pegalajar MC (2001) A real-coded genetic algorithm for training recurrent neural networks. Neural Netw 14(1):93–105
Conejo AJ, Plazas MA, 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
Connor J, Atlas L (1991) Recurrent neural networks and time series prediction. In: IJCNN-91-Seattle international joint conference on neural networks, 1991, vol 1. IEEE, pp 301–306
Feijoo F, Silva W, Das TK (2016) A computationally efficient electricity price forecasting model for real time energy markets. Energy Convers Manag 113:27–35
Gao W, Sarlak V, Parsaei MR, Ferdosi M (2018) Combination of fuzzy based on a meta-heuristic algorithm to predict electricity price in an electricity markets. Chem Eng Res Des 131:333–345
Gholipour Khajeh M, Maleki A, Rosen MA, Ahmadi MH (2018) Electricity price forecasting using neural networks with an improved iterative training algorithm. Int J Ambient Energy 39(2):147–158
Gollou AR, Ghadimi N (2017) A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. J Intell Fuzzy Syst 32(6):4031–4045
Hannah Jessie Rani R, Aruldoss Albert Victoire T (2018) Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer. PLoS ONE 13(5):1–35
He S, Wu QH, Saunders JR (2009a) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evolut Comput 13(5):973–990
He S, Wu QH, Saunders JR (2009b) Breast cancer diagnosis using an artificial neural network trained by group search optimizer. Trans Inst Meas Control 31(6):517–531
Hong Y-Y, Hsiao C-Y (2002) Locational marginal price forecasting in deregulated electricity markets using artificial intelligence. IEE Proc Gener Transm Distrib 149(5):621–626
Jianwei E, Bao Y, Ye J (2017) Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis. Phys A Stat Mech Appl 484:412–427
Keles D, Scelle J, Paraschiv F, Fichtner W (2016) Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl Energy 162:218–230
Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, Boston, pp 760–766
Lahmiri S (2017) Comparing variational and empirical mode decomposition in forecasting day-ahead energy prices. IEEE Syst J 11(3):1907–1910
Liu H, Mi X, Li Y (2018) Comparison of two new intelligent wind speed forecasting approaches based on wavelet packet decomposition, complete ensemble empirical mode decomposition with adaptive noise and artificial neural networks. Energy Convers Manag 155:188–200
Maciejowska K, Weron R (2016) Short-and mid-term forecasting of baseload electricity prices in the UK: the impact of intra-day price relationships and market fundamentals. IEEE Trans Power Syst 31(2):994–1005
Maciejowska K, Nowotarski J, Weron R (2016) Probabilistic forecasting of electricity spot prices using factor quantile regression averaging. Int J Forecast 32(3):957–965
Mandal P, Senjyu T, Funabashi T (2006) Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market. Energy Convers Manag 47(15–16):2128–2142
Mandic DP, Chambers J (2001) Recurrent neural networks for prediction: learning algorithms, architectures and stability. Wiley, Hoboken
Nowotarski J, Weron R (2016) On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Energy Econ 57:228–235
Panapakidis IP, Dagoumas AS (2016) Day-ahead electricity price forecasting via the application of artificial neural network based models. Appl Energy 172:132–151
Rafiei M, Niknam T, Khooban MH (2016) A novel intelligent strategy for probabilistic electricity price forecasting: wavelet neural network based modified dolphin optimization algorithm. J Intell Fuzzy Syst 31(1):301–312
Rafiei M, Niknam T, Khooban MH (2017a) Probabilistic electricity price forecasting by improved clonal selection algorithm and wavelet preprocessing. Neural Comput Appl 28(12):3889–3901
Rafiei M, Niknam T, Khooban M-H (2017b) Probabilistic forecasting of hourly electricity price by generalization of ELM for usage in improved wavelet neural network. IEEE Trans Ind Inform 13(1):71–79
Ravindran S, Aruldoss Albert Victoire T (2018) A bio-geography-based algorithm for optimal siting and sizing of distributed generators with an effective power factor model. Comput Electr Eng 72:482–501
Shayeghi H, Ghasemi A, Moradzadeh M, Nooshyar M (2017) Day-ahead electricity price forecasting using WPT, GMI and modified LSSVM-based S-OLABC algorithm. Soft Comput 21(2):525–541
Shrivastava NA, Khosravi A, Panigrahi BK (2015) Prediction interval estimation of electricity prices using PSO-tuned support vector machines. IEEE Trans Ind Inform 11(2):322–331
Shrivastava NA, Panigrahi BK, Lim M-H (2016) Electricity price classification using extreme learning machines. Neural Comput Appl 27(1):9–18
Tan Z, Zhang J, Wang J, Xu J (2010) Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Appl Energy 87(11):3606–3610
Wang J, Zhang W, Li Y, Wang J, Dang Z (2014) Forecasting wind speed using empirical mode decomposition and Elman neural network. Appl Soft Comput 23:452–459
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
Wang D, Luo H, Grunder O, Lin Y, Guo H (2017a) 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
Wang D, Luo H, Grunder O, Lin Y (2017b) Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction. Renew Energy 113:1345–1358
Wang D, Wei S, Luo H, Yue C, Grunder O (2017c) A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine. Sci Total Environ 580:719–733
Weron R (2014) Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int J Forecast 30(4):1030–1081
Xu L, Dong ZY, Tay A (2004) Time series forecast with Elman neural networks and genetic algorithms. In: Tan KC, Lim MH, Yao X, Wang L (eds) Recent advances in simulated evolution and learning, Advances in natural computation, vol 2. World Scientific, Singapore, pp 747–768. https://doi.org/10.1142/9789812561794_0040
Yang Z, Ce L, Lian L (2017) Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Appl Energy 190:291–305
Zhang J, Tan Z (2013) Day-ahead electricity price forecasting using WT, CLSSVM and EGARCH model. Int J Electr Power Energy Syst 45(1):362–368
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Hannah Jessie Rani, R., Aruldoss Albert Victoire, T. A hybrid Elman recurrent neural network, group search optimization, and refined VMD-based framework for multi-step ahead electricity price forecasting. Soft Comput 23, 8413–8434 (2019) doi:10.1007/s00500-019-04161-6
- Spot-price forecasting
- Variational mode decomposition
- Particle swarm optimization
- Group search optimization
- Elman recurrent neural network