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Research Article
Review on Design and Simulation of Electricity Price Fore Casting Using Artificial Neural Network
Damini Kamble1
Ganesh Wakte2
1PG Scholar, Department of Electrical Engineering, Tulsiraimji Patil College of Engineering, Nagpur, Maharashtra, India. 2Associate Professor, Department of Electrical Engineering, Tulsiraimji Patil College of Engineering, Nagpur, Maharashtra, India.
Published Online: March-April 2024
Pages: 279-282
Cite this article
↗ https://www.doi.org/10.59256/ijire.20240502037
References
1. V. Gundu and S. P. Simon, “PSO–LSTM for short term forecast of heterogeneous time series electricity price signals,” J. Ambient
Intell. Humaniz. Comput., Jul. 2020.
2. J. Olamaee, M. Mohammadi, A. Noruzi, and S. M. H. Hosseini, “Day- ahead price forecasting based on hybrid prediction model,”
Complexity, vol. 21, no. S2, pp. 156– 164, Nov. 2016.
3. G. Mestre, J. Portela, A. Muñoz San Roque, and E. Alonso, “Forecasting hourly supply curves in the Italian Day-Ahead electricity
market with a doubleseasonal SARMAHX model,” Int. J. Electr. Power Energy Syst., vol. 121, p. 106083, Oct. 2020.
4. T. Pinto, T. M. Sousa, I. Praça, Z. Vale, and H. Morais, “Support Vector Machines for decision support in electricity markets strategic
bidding,” Neurocomputing, vol. 172, pp. 438–445, Jan. 2016.
5. X. Qiu, P. N. Suganthan, and G. A. J. Amaratunga, “Short-term Electricity Price Forecasting with Empirical Mode Decomposition
based Ensemble Kernel Machines,” Procedia Comput. Sci., vol. 108, pp. 1308–1317, 2017.
6. H. Shayeghi and A. Ghasemi, “Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based
scheme,” Energy Convers. Manag., vol. 74, pp. 482–491, Oct. 2013.
7. V. Sharma and D. Srinivasan, “A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price
prediction in deregulated electricity market,” Eng. Appl. Artif. Intell., vol. 26, no. 5–6, pp. 1562–1574, 2013.
8. N. Amjady and F. Keynia, “Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded
neural network technique,” Energy Convers. Manag., vol. 50, no. 12, pp. 2976–2982, 2009.
9. N. Amjady and A. Daraeepour, “Design of input vector for day-ahead price forecasting of electricity markets,” Expert Syst. Appl., vol.
36, no. 10, pp. 12281– 12294, Dec. 2009.
10. F. Keynia, “A new feature selection algorithm and composite neural network for electricity price forecasting,” Eng. Appl. Artif. Intell.,
vol. 25, no. 8, pp. 1687–1697, 2012.
11. J. Padhye, V. Firoiu, and D. Towsley, “A stochastic model of TCP Reno congestion avoidance and control,” Univ. of Massachusetts,
Amherst, MA, CMPSCI Tech. Rep. 99-02, 1999.
12. Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, IEEE Std. 802.11, 1997.
Intell. Humaniz. Comput., Jul. 2020.
2. J. Olamaee, M. Mohammadi, A. Noruzi, and S. M. H. Hosseini, “Day- ahead price forecasting based on hybrid prediction model,”
Complexity, vol. 21, no. S2, pp. 156– 164, Nov. 2016.
3. G. Mestre, J. Portela, A. Muñoz San Roque, and E. Alonso, “Forecasting hourly supply curves in the Italian Day-Ahead electricity
market with a doubleseasonal SARMAHX model,” Int. J. Electr. Power Energy Syst., vol. 121, p. 106083, Oct. 2020.
4. T. Pinto, T. M. Sousa, I. Praça, Z. Vale, and H. Morais, “Support Vector Machines for decision support in electricity markets strategic
bidding,” Neurocomputing, vol. 172, pp. 438–445, Jan. 2016.
5. X. Qiu, P. N. Suganthan, and G. A. J. Amaratunga, “Short-term Electricity Price Forecasting with Empirical Mode Decomposition
based Ensemble Kernel Machines,” Procedia Comput. Sci., vol. 108, pp. 1308–1317, 2017.
6. H. Shayeghi and A. Ghasemi, “Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based
scheme,” Energy Convers. Manag., vol. 74, pp. 482–491, Oct. 2013.
7. V. Sharma and D. Srinivasan, “A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price
prediction in deregulated electricity market,” Eng. Appl. Artif. Intell., vol. 26, no. 5–6, pp. 1562–1574, 2013.
8. N. Amjady and F. Keynia, “Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded
neural network technique,” Energy Convers. Manag., vol. 50, no. 12, pp. 2976–2982, 2009.
9. N. Amjady and A. Daraeepour, “Design of input vector for day-ahead price forecasting of electricity markets,” Expert Syst. Appl., vol.
36, no. 10, pp. 12281– 12294, Dec. 2009.
10. F. Keynia, “A new feature selection algorithm and composite neural network for electricity price forecasting,” Eng. Appl. Artif. Intell.,
vol. 25, no. 8, pp. 1687–1697, 2012.
11. J. Padhye, V. Firoiu, and D. Towsley, “A stochastic model of TCP Reno congestion avoidance and control,” Univ. of Massachusetts,
Amherst, MA, CMPSCI Tech. Rep. 99-02, 1999.
12. Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, IEEE Std. 802.11, 1997.
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