Abstract
In a deregulated electricity market scenario, formulation of bidding strategies and investment decisions depends majorly on forecasting of Market Clearing Price (MCP). This research proposes and compares models based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks to achieve the same. Data for training and testing of the proposed models is collected from Indian Energy Exchange (IEX). Trained models are used to perform day-ahead and week-ahead predictions. Mean Absolute Percentage Error (MAPE) for each model in each case is calculated. Results show that both LSTM- and GRU-based models deliver a reasonably good overall performance with LSTM performing slightly better.
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References
Rodriguez, C., Anders, G.: Bidding strategy design for different types of electric power market participants. IEEE Trans. Power Syst. 19, 964–971 (2004)
Bunn, D., Karakatsani, N.: Forecasting electricity prices. Review paper. London Business School Working Paper (2003)
Gonzalez, A., Roque, A., Garcia-Gonzalez, J.: Modeling and forecasting electricity prices with input/output hidden Markov models. IEEE Trans. Power Syst. 20, 13–24 (2005)
Box, G., Jenkins, G., Reinsel, G.: Time Series Analysis. Prentice Hall, Englewood Cliffs (1994)
Pindoriya, N., Singh, S., Singh, S.: An adaptive wavelet neural network-based energy price forecasting in electricity markets. IEEE Trans. Power Syst. 23, 1423–1432 (2008)
Khan, G., Arshad, R., Khan, N.: Efficient prediction of dynamic tariff in smart grid using CGP evolved artificial neural networks. In: 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 493–498. IEEE, Cancun (2017)
Alshejari, A., Kodogiannis, V.: Electricity price forecasting using asymmetric fuzzy neural network systems. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE, Naples (2017)
Yang, H., Duan, X.: Chaotic characteristics of electricity price and its forecasting model. In: Canadian Conference on Electrical and Computer Engineering—CCECE 2003, pp. 659–662. IEEE (2003)
Anamika, Kumar, N.: Market clearing price prediction using ANN in Indian electricity markets. In: International Conference on Energy Efficient Technologies for Sustainability (ICEETS), pp. 454–458. IEEE, Nagercoil, India (2016)
Indian Energy Exchange. https://www.iexindia.com/
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. NIPS 2014 Workshop on Deep Learning, December 2014 (2014)
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Ubrani, A., Motwani, S. (2019). LSTM- and GRU-Based Time Series Models for Market Clearing Price Forecasting of Indian Deregulated Electricity Markets. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 898. Springer, Singapore. https://doi.org/10.1007/978-981-13-3393-4_70
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DOI: https://doi.org/10.1007/978-981-13-3393-4_70
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