Forecasting the Electricity Price Emphasizing Prices Jumps by Using Combination of Neural and Fuzzy Network with Particle Swarm Optimization Algorithm

Document Type: Research Paper

Authors

Department of Economics, Kharazmi University, Tehran, Iran

Abstract

After deregulation in electricity markets, lots of studies were conducted especially in designing new systems and energy pricing in order to improve efficiency of power systems and increase investors’ profit. Investment’s profit could be increased by better contracts and better price bidding for buying and selling energy in electricity market, as a consequence price forecasting is essential. The main objective of this paper is to predict the price of electricity in Iran’s electricity market by using a combination of fuzzy-neural network and particle swarm optimization (PSO). In this paper, past prices, past loads, working and nonworking days, day hours and effect of seasons in 2015 have been taken into account as the effective factors in forecasting mechanism. The combined model is more precise than other methods like ARIMA, neural network, neural-fuzzy network, and a combination of fuzzy-neural and genetic algorithm. In the following, the process of price fluctuations has been discussed for increasing effectiveness of bidding. Results of simulation revealed that price forecasting is much more precise with price process mechanism.

Keywords

Main Subjects


1. Yamin, H., Shahidehpour, S., and Li, Z. (2004). “Adaptive Short-Term Electricity Price Forecasting Using Artificial Neural Networks in Restructured Power Markets”, Electrical Power and Energy Systems, Vol. 26, No. 8, PP. 571-581.
2. Azevedo, F., and Vale, Z. (2006). “Forecasting Electricity Prices with Historical Statistical Information Using Neural Networks and Clustering Techniques”, Proceeding of IEEE PES Power Systems Conference and Exposition, Georgia, USA, PP. 44-50.
3. Hu, L., Taylor, G., Wan, H., and Irving, M. (2009). “A Review of Short-Term Electricity Price Forecasting Techniques in Deregulated Electricity Markets”, Proceeding of 44th International Universities Power Engineering Conference, Glasgow, UK, PP. 1-5.
4. Voronin, S., and Partanen, J. (2013). “Forecasting Electricity Price and Demand Using a Hybrid Approach Based on Wavelet Transform, ARIMA and Neural Networks”, International Jornal of Energy Research, Vol. 38, No. 5, PP. 626-637
5. Rao, N., and Sarada, K. (2013). “Price Eastimation for Day-Ahead Electricity Market Using Fuzzy Logic”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, No. 5, PP. 1940-1946.
6. Shrivastava, N., and Panigrahi, B. (2014). “A Hybrid Wavelet-ELM Based Short Term Price Forecasting for Electricity Markets”, International Journal of Electrical Power and Energy Systems, Vol. 55, No. 1, PP. 41-50.
7. Shafie Khah, M., Parsamoghaddam, M., and Sheikh-el-Eslami, M. (2011). “Price Forecasting of Day-Ahead Electricity Markets Using a Hybrid Forecast Method”, Energy Conversion and Management, Vol. 52, No. 5, PP. 2165-2169.
8. Young, D., Poletti, S., and Browne, O. (2014). “Can Agent-Based Models Forecast Spot Prices in Electricity Markets? Evidence from the New Zealand Electricity Market”, Energy Economics, Vol. 45, No. 1, PP. 419-434.
9. Keles, D., Scelle, J., Paraschiv, F., and Fichtner, W. (2016). “Extended Forecast Methods for Day-Ahead Electricity Spot Prices Applying Artificial Neural Networks”, Applied Energy, Vol. 162, No. 1, PP. 218-230.
10. Yang, Z., Ce, L., and Ce, L. (2017). “Electricity Price Forecasting by a Hybrid Model, Combining Wavelet Transform, ARMA and Kernel-Based Extreme Learning Machine Methods”, Applied Energy, Vol. 190, No. 1, PP. 291-305.
11. Jang, J. S. R. (1993). “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Transaction On System, Vol. 23, No. 3, PP. 665-685.
12. Clerc, M. (2006). Particle Swarm Optimization. British Library Cataloguing In Publication Data, London.
13. Kennedy, J.,  and Eberhart, R. (1995). “Particle Swarm Optimization.” Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, PP. 1942–1948.
14.Aggarwal, S., Saini, L., and Kumar, A. (2009). “Electricity price Forecasting in Deregulated Markets:A Reviewand Evaluation”, International Journal of Electrical Power & Energy Systems, Vol. 31, No. 1, PP. 13-29.