Abstract
The Indian power exchange market is rapidly evolving. As a result of which consistent forecasting of Market Clearance Price (MCP) has become a very crucial task for trading electricity to any part of the country. An analysis of the fifteen-minute MCP forecasting of the Indian Energy Exchange (IEX) is done. Firstly, only a single data point is predicted using Long-Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) based RNN. Furthermore, twenty data points which represent five hours ahead forecasting are done using both models. The comparative analysis between the experimental results and the real data from IEX proves the effectiveness of the developed models that GRU is better when it comes to speed and memory, but in terms of accuracy LSTM has got a slight edge over GRU.
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References
IEX Power Market Update, November 2022. https://www.iexindia.com/Uploads/NewsUpdate/06122022IEX%20POWER%20MARKET%20UPDATE_Nov%202022_Final.pdf
Koltsaklis NE, Dagoumas AS (2020) An optimization model for integrated portfolio management in wholesale and retail power markets. J Clean Prod 248:119198. ISSN 0959-6526. https://doi.org/10.1016/j.jclepro.2019.119198
Liu H, Shi J (2013) Applying ARMA–GARCH approaches to forecasting short-term electricity prices. Energy Econ 37:152–166. ISSN 0140-9883. https://doi.org/10.1016/j.eneco.2013.02.006
Garcia RC, Contreras J, van Akkeren M, Garcia JBC (2005) A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans Power Syst 20(2):867–874. https://doi.org/10.1109/TPWRS.2005.846044
Rajan P, Chandrakala KRMV (2021) Statistical model approach of electricity price forecasting for Indian electricity market. In: 2021 IEEE Madras section conference (MASCON), Chennai, India, pp 1–5. https://doi.org/10.1109/MASCON51689.2021.9563474
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. https://doi.org/10.1109/TPWRS.2005.846054
Li G, Liu C-C, Lawarree J, Gallanti M, Venturini A (2005) State-of-the-art of electricity price forecasting. In: International symposium CIGRE/IEEE PES, 2005, New Orleans, LA, USA, pp 110–119. https://doi.org/10.1109/CIGRE.2005.1532733
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. https://doi.org/10.1109/TPWRS.2004.840416
Anamika, Kumar N (2016) Market clearing price prediction using ANN in Indian electricity markets. In: 2016 international conference on energy efficient technologies for sustainability (ICEETS), Nagercoil, India, pp 454–458. https://doi.org/10.1109/ICEETS.2016.7583797
Boru İpek A (2021) Prediction of market-clearing price using neural networks based methods and boosting algorithms. Int Adv Res Eng J 5(2):240–246. https://doi.org/10.35860/iarej.824168
Chaudhury P, Tyagi A, Shanmugam PK (2020) Comparison of various machine learning algorithms for predicting energy price in open electricity market. In: 2020 international conference and utility exhibition on energy, environment and climate change (ICUE), Pattaya, Thailand, pp 1–7. https://doi.org/10.1109/ICUE49301.2020.9307100
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Dey, M.K., Chanana, S. (2024). Comparison of LSTM and GRU for Predicting Market Clearing Price in Open Electricity Market. In: Kumar, A., Singh, S.N., Kumar, P. (eds) Decarbonisation and Digitization of the Energy System. SGESC 2023. Lecture Notes in Electrical Engineering, vol 1099. Springer, Singapore. https://doi.org/10.1007/978-981-99-7630-0_23
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DOI: https://doi.org/10.1007/978-981-99-7630-0_23
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